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Fundamental diagram of traffic flow

The fundamental diagram of traffic flow is a foundational construct in transportation engineering that empirically relates three macroscopic traffic variables—density k (vehicles per unit length), flow rate q (vehicles per unit time), and space-mean speed v (average vehicle speed)—via the continuity equation q = k \cdot v.^[1] In the flow-density representation, it characteristically displays a unimodal curve: flow rises with increasing density under uncongested conditions to a maximum capacity point, after which flow declines toward zero as density nears the jam density where vehicles are stationary.^[1] First formulated by Bruce D. Greenshields in 1935 through field observations assuming a linear decline in speed with density, yielding a parabolic flow-density profile, the diagram has since been adapted with alternative shapes like triangular forms to accommodate empirical variations, including data scatter, stochasticity, and regime-dependent behaviors observed in real roadways.^[2]^[3] Despite these refinements revealing deviations from determinism—such as hysteresis between acceleration and deceleration phases—the fundamental diagram underpins kinematic wave theory, macroscopic simulation models like the Lighthill-Whitham-Richards framework, and practical applications in capacity estimation, bottleneck analysis, and adaptive traffic management.^[4]

History

Origins in Early Traffic Studies

The scientific investigation of traffic flow as a quantifiable phenomenon emerged in the early 20th century amid rising automobile usage, though systematic empirical studies remained scarce before the 1930s. One precursor effort involved aerial photography conducted by Johnson in 1928, who analyzed traffic between Baltimore and Washington, D.C., using 127 overlapping photographs taken from an airplane at 3,600 feet altitude to measure vehicle density, speed, and clearance distances. This yielded an empirical relationship between clearance C (in feet) and speed V (in mph) expressed as C = 1.3 \times V^{0.5}, providing an initial insight into spacing dynamics under varying speeds but without formalizing flow-density linkages.^[2] Pioneering quantitative studies commenced with Bruce D. Greenshields' work in the early 1930s at the Yale Bureau of Highway Traffic, marking the foundational empirical basis for the fundamental diagram. In 1933, Greenshields presented methods for observing traffic behavior using photography at the Highway Research Board meeting, emphasizing precise measurement of speeds and volumes. By 1934, during his PhD research at the University of Michigan, he conducted field analyses relating traffic volume to speeds, employing innovative techniques such as a 16-mm movie camera synchronized with an electric motor to capture data at intervals of 0.5 to 2 seconds, supplemented by ground measurements with a 100-foot tape and photographed markers. Aerial observations from a blimp at 2,000 feet further enabled recording of speed, density, and flow across extended road segments.^[2] Greenshields' seminal 1935 publication, "A Study of Traffic Capacity," synthesized these efforts into the core relationships underpinning the fundamental diagram, introducing the continuity equation q = k \times v where q denotes flow (vehicles per hour), k density (vehicles per mile), and v space-mean speed (mph). He postulated a linear speed-density model v = v_f (1 - k/k_j), with free-flow speed v_f = 46 mph and jam density k_j = 195 vehicles per mile, yielding a parabolic flow-density curve peaking at maximum capacity of 2,240 vehicles per hour when density reached 98 vehicles per mile and speed 23 mph. These findings distinguished stable (low-density) from unstable (high-density) traffic regimes, laying the groundwork for capacity estimation despite reliance on homogeneous, uninterrupted flow assumptions derived from limited rural highway data.^[2]

Development of Key Models (1930s–1960s)

Bruce D. Greenshields developed the inaugural quantitative model for traffic stream characteristics in 1935, based on empirical observations of vehicle speeds and spacings on rural highways. Using aerial photography to measure speeds at varying densities, he identified a linear relationship between average speed v and density k: v = v_f \left(1 - \frac{k}{k_j}\right), where v_f is the free-flow speed (typically 70-80 km/h in his data) and k_j is the maximum jam density (around 150-200 vehicles per km).^[5] ^[3] This formulation implied a parabolic flow-density relationship q = v_f k \left(1 - \frac{k}{k_j}\right), with maximum flow q_{\max} = \frac{v_f k_j}{4} occurring at critical density k_c = \frac{k_j}{2}.^[2] The model assumed homogeneous driver behavior and uniform vehicle characteristics, deriving from first-principles observation that speed diminishes proportionally with vehicle occupancy of roadway space.^[6] Greenshields' work, detailed in his Highway Research Board paper "A Study of Traffic Capacity," established the fundamental diagram as a tool for capacity estimation, influencing highway design standards through the 1950s. Empirical data from Connecticut and Ohio roads validated the linearity for uncongested conditions, though later analyses revealed overestimation of speeds at high densities.^[5] ^[7] Despite these limitations, the model's simplicity facilitated its adoption in early traffic engineering texts and simulations, providing a baseline for predicting breakdown flows around 1,800-2,200 vehicles per hour per lane.^[2] By the late 1950s, accumulated datasets from urban and freeway observations highlighted nonlinear deviations, prompting refinements while the linear form persisted for its analytical tractability.^[6] In the 1960s, alternatives emerged to address the linear model's poor fit to congested regimes, where speeds plateau near zero rather than extrapolating unrealistically. R.T. Underwood proposed an exponential speed-density model in 1961: v = v_f \exp\left(-\frac{k}{\alpha}\right), with parameter \alpha (often 30-50 vehicles per km) calibrating the decay rate from empirical freeway data.^[8] ^[9] This yielded a flow-density curve q = v_f k \exp\left(-\frac{k}{\alpha}\right), peaking at q_{\max} = v_f \alpha / e for low critical densities, better capturing free-flow efficiency but requiring hybrid extensions for jam conditions.^[10] Such models reflected causal insights into driver perception-response limits, diverging from Greenshields' uniform spacing assumption toward logarithmic sensitivity to density increases.^[11] These developments laid groundwork for distinguishing free-flow and forced-flow branches, though deterministic forms remained debated due to data scatter from heterogeneous traffic.^[2]

Evolution to Macroscopic Approaches (1970s–2000s)

In the 1970s, macroscopic traffic flow modeling advanced with second-order formulations that extended the kinematic wave theory of Lighthill, Whitham, and Richards by incorporating drivers' anticipation of downstream conditions and inertial effects. The Payne-Whitham model, introduced by H.J. Payne in 1971, represented traffic as a compressible fluid using two coupled partial differential equations: one for vehicle conservation and another for momentum balance, which included terms for relaxation toward equilibrium speed and anticipation based on downstream density gradients.^[12] This approach enabled simulation of traffic instabilities, such as stop-and-go waves, that first-order models could not capture without diffusion approximations.^[13] By the late 1970s, attention shifted toward urban networks, where intersections and signals introduce frequent disruptions absent in freeway models. In 1979, Robert Herman and Ilya Prigogine developed a two-fluid macroscopic model distinguishing "fast" (moving) vehicles from "slow" or stopped ones, drawing from kinetic theory to describe aggregate flow in grid-like urban environments.^[14] This framework treated stopped vehicles as a background fluid influencing moving flow rates, allowing estimation of network-wide performance metrics like average trip times under varying turning proportions and signal timings, with empirical calibration showing saturation flows around 1,800–2,200 vehicles per hour per lane in U.S. cities.^[15] The 1980s and 1990s saw computational refinements for practical implementation, culminating in discrete approximations suitable for dynamic network assignment. Carlos Daganzo's cell transmission model (CTM), proposed in 1994, discretized highways into uniform cells and propagated flows via supply-demand constraints derived from the fundamental diagram's concave shape, ensuring consistency with hydrodynamic theory while enabling efficient simulation of multi-commodity network flows.^[16] The CTM's sending-flow and receiving-flow rules, bounded by wave speeds (typically 15–20 km/h backward waves), facilitated real-time applications like ramp metering, with validations demonstrating accuracy in replicating observed densities up to jam levels of 150–200 vehicles per km per lane.^[17] These macroscopic advancements emphasized aggregate variables—density, flow, and speed—over individual trajectories, prioritizing causal mechanisms like capacity constraints and wave propagation for predictive reliability in congested regimes.^[18]

Core Relationships

Speed-Density Relationship

The speed-density relationship in the fundamental diagram of traffic flow quantifies the inverse monotonic association between average vehicle speed v and traffic density k, where density represents vehicles per unit length of roadway. At zero density, speed attains the free-flow value v_f, typically 80–120 km/h on highways depending on design and conditions; as density rises toward jam density k_j (around 150–200 vehicles/km for passenger cars, accounting for vehicle lengths of 5–7 m plus safety gaps), speed declines to near zero due to physical constraints on spacing and interactions. This relationship derives causally from increased vehicle proximity enforcing reduced speeds to maintain safe headways, with empirical validations from loop detectors and aerial observations confirming the pattern across facilities, though scatter arises from heterogeneity in driver behavior and vehicle types.^[19]^[1]^[6] The foundational linear formulation, introduced by Bruce Greenshields in 1935 based on field data from rural highways, posits v = v_f (1 - k/k_j), implying a constant sensitivity of speed to density and deriving flow-density via q = k v = v_f k (1 - k/k_j). This deterministic model assumes uniform driver populations and equilibrium conditions, yielding a triangular flow-density profile with maximum flow at k = k_j/2. Calibrations to U.S. Interstate data yield v_f \approx 105 km/h and k_j \approx 185 veh/km, but the linearity overpredicts speeds at high densities where empirical curves exhibit concavity from behavioral adaptations like platooning.^[20]^[21]^[22] Empirical speed-density curves, derived from inductive loop sensors on freeways like California's PeMS network, reveal non-linear forms such as exponential decay v = v_f \exp(-a k) or logarithmic variants, better capturing the gradual initial drop in speed (e.g., <5% reduction up to 20–30 veh/km) followed by abrupt declines near capacity (80–100 veh/km). Studies of heterogeneous traffic, including trucks, show steeper gradients and lower v_f due to length effects on spacing, with waves propagating faster through longer vehicles; for instance, data from urban arterials indicate adjusted k_j values of 120–140 veh/km when factoring 10–20% heavy vehicles. Variability persists, with standard deviations of 10–20 km/h at moderate densities attributable to stochastic following rules rather than systemic biases in measurement.^[23]^[24]^[25] Advanced extensions incorporate microscopic factors like time headway \tau (1.5–2 s minimum) and vehicle length l, yielding k_j = 1/(l + v \tau) at equilibrium, validated against Next Generation SIMulation data showing deviations from linearity exceeding 15% at k > 100 veh/km. While Greenshields remains computationally tractable for macroscopic simulations, logarithmic or pipe-flow models (e.g., Greenberg's v = v_f \exp(-1/k_c k), with critical density k_c) align closer to observed asymmetries in breakdown transitions, as confirmed by 2015–2020 European motorway datasets.^[26]^[22]^[27]

Flow-Density Relationship

The flow-density relationship in traffic flow theory illustrates the variation of traffic flow rate q (vehicles per hour per lane) with respect to vehicle density k (vehicles per kilometer per lane), forming a key element of the fundamental diagram. This relationship typically exhibits an inverted U-shape: flow initiates at zero for zero density, rises to a peak capacity q_max at critical density k_c, then declines to zero at jam density k_j, beyond which vehicles halt completely.^[1] The ascending branch corresponds to free-flow conditions where increasing density yields higher throughput until instability onset, while the descending branch reflects congested states with reduced speeds dominating flow.^[6] Empirical observations derive from loop detectors and aerial surveys, revealing maximum flows around 2,000–2,400 vehicles per hour per lane on highways, with k_c approximately 20–30 vehicles per kilometer per lane.^[19] Jam densities commonly range from 140 to 185 vehicles per kilometer per lane, influenced by vehicle composition, lengths averaging 5–7 meters for passenger cars plus safety gaps of 1–2 meters.^[28] Data scatter arises from factors like driver behavior, vehicle heterogeneity, and measurement errors, precluding a universal deterministic curve and prompting fits to envelopes or averages.^[26] The foundational Greenshields model, introduced in 1935 based on field data from rural roads, posits a parabolic form q = v_f k (1 – k/k_j), stemming from an assumed linear speed-density decline where free-flow speed v_f approximates 80–110 km/h.^[29] This yields q_max = v_f k_j/4 at k_c = k_j/2, aligning qualitatively with observations though overestimating capacities in dense urban settings due to neglected behavioral instabilities. Subsequent refinements, such as the triangular model, approximate the curve with linear uncongested and congested branches meeting at capacity, facilitating analytical solutions in queueing and simulation contexts.^[30] Hysteresis effects, where flow differs for rising versus falling densities, have been noted in empirical loops, attributed to stop-and-go waves but absent in equilibrium assumptions.^[28]

Speed-Flow Relationship

The speed-flow relationship in traffic flow theory describes the variation in average vehicle speed v with respect to flow rate q (vehicles per unit time), derived from the fundamental continuity equation q = k \cdot v, where k is density (vehicles per unit length), implying v = q / k.^[19] This bivariate curve forms part of the fundamental diagram, representing equilibrium states under steady conditions, though real-world data exhibits hysteresis and non-equilibrium effects.^[1] In the uncongested regime, empirical data indicate that speed remains approximately constant at or near the free-flow speed v_f (typically 80–120 km/h on highways) for low to moderate flows, as vehicle interactions are minimal and spacing allows unimpeded travel.^[6] As flow approaches roadway capacity (often 1,800–2,400 vehicles per lane per hour), speed begins to decline, reflecting increased density and interactions; beyond capacity, in the congested regime, small increments in flow lead to sharp speed reductions due to queue formation and reduced headways.^[1] Generalized empirical shapes, as proposed by Hall, Hurdle, and Banks in 1992 based on North American freeway data, feature a near-horizontal segment at low flows followed by a concave decline and abrupt drop near capacity, contrasting with smoother theoretical curves.^[6] Theoretical models yield specific functional forms for the speed-flow curve. The Greenshields model (1935), assuming a linear speed-density relation v = v_f (1 - k/k_j) with jam density k_j (typically 150–200 vehicles/km/lane), produces a speed-flow equation solved quadratically as v = \frac{1}{2} \left[ v_f + \sqrt{v_f^2 - 4 (v_f / k_j) q} \right], resulting in a monotonically decreasing, concave curve from v_f at q=0 to 0 at maximum flow q_{\max} = v_f k_j / 4.^[1] More advanced piecewise models, such as Smulders' (with free-flow linear segment and hyperbolic congested branch) or De Romph's (incorporating power-law adjustments), better approximate empirical plateaus by fitting parameters to observed data, achieving capacities around 2,200 vehicles/h/lane for European motorways with v_f \approx 110 km/h.^[1] Empirical curves derived from loop detector data show considerable scatter, with speed variance increasing at higher flows due to stochastic driver behaviors, vehicle heterogeneity, and environmental factors; concave fits often outperform linear ones, especially at low volumes where data cluster near v_f.^[31] Capacity drops of 10–20% post-congestion onset, as in Wu's model calibrated to real data, further distort the curve, emphasizing the need for regime-specific calibrations over single equilibrium assumptions.^[1] These relationships inform capacity estimation and control strategies but require site-specific validation, as shapes vary by facility type (e.g., shallower declines on multilane highways versus urban arterials).^[19]

Theoretical Models

Deterministic Formulations

Deterministic formulations of the fundamental diagram model traffic flow under assumptions of steady-state equilibrium, spatial homogeneity, and deterministic relationships among speed (v), density (k), and flow (q = k v), without stochastic variability or time-dependent fluctuations. These models, primarily macroscopic, derive the flow-density relation q = Q(k) from an underlying speed-density function v(k), often calibrated to empirical data from single facilities like highways. Pioneered in the mid-20th century, they provide analytical tractability for applications such as shockwave analysis in the Lighthill-Whitham-Richards (LWR) model, though they simplify complex driver behaviors.^[6]^[2] The Greenshields model, introduced in 1935, posits a linear decrease in speed with density: v(k) = v_f \left(1 - \frac{k}{k_j}\right), where v_f is free-flow speed and k_j is jam density. This yields a parabolic flow-density curve q(k) = v_f k \left(1 - \frac{k}{k_j}\right), with maximum capacity q_{\max} = \frac{v_f k_j}{4} at critical density k_c = \frac{k_j}{2}. Derived from photographic observations of rural highways, the model assumes uniform driver response and symmetric backward-propagating waves, fitting early data but overestimating flows near jam conditions due to its single-regime nature.^[32]^[6] Subsequent refinements addressed empirical asymmetries. The Underwood model (1961) employs an exponential speed-density relation v(k) = v_f \exp\left(-\alpha \frac{k}{k_j}\right), where \alpha > 0 controls deceleration, producing a flow-density curve that decays more gradually at high densities and better matches observed near-free-flow behaviors on multilane roads. Greenberg's 1959 logarithmic formulation, v(k) = v_f \exp\left(-\frac{1}{2\alpha} \ln\frac{k}{k_j}\right), draws from fluid dynamics analogies, emphasizing hyperbolic flow-density shapes suitable for congested regimes but less accurate at low densities. These single-regime models prioritize simplicity for calibration via least-squares fitting to loop detector data.^[33]^[22]^[30] Multi-regime deterministic models, such as the triangular fundamental diagram, segment the curve into free-flow (linear q = v_f k) and congested (linear with constant wave speed w) branches, meeting at capacity q_{\max} and critical density k_c. Parameterized by v_f, w, k_j, and q_{\max}, it simplifies LWR numerics and aligns with cellular automaton simulations, though it assumes abrupt transitions unsupported by granular data. Variants like the piecewise-linear Drake model extend this for better empirical fidelity across densities. Calibration typically uses maximum likelihood on aggregated sensor measurements, revealing capacities around 2,000–2,500 veh/h/ln on U.S. freeways.^[34]^[2] These formulations underpin hydrodynamic traffic simulations but rely on idealized equilibrium, often diverging from real-world scatter due to unmodeled heterogeneity in vehicle types or incidents.^[27]^[6]

Stochastic and Non-Equilibrium Extensions

Stochastic extensions to the fundamental diagram incorporate randomness arising from driver heterogeneity, perception errors, and environmental fluctuations, which generate the empirical scatter observed in flow-density data beyond deterministic averages. These models represent traffic variables such as flow and density as probabilistic distributions rather than fixed functions, often using stochastic differential equations or Gaussian processes to capture variance in macroscopic relations. For instance, the stochastic fundamental diagram (SFD) quantifies mean flow as a function of density alongside confidence intervals derived from Monte Carlo simulations of microscopic behaviors, improving predictions of breakdown probabilities compared to deterministic curves.^[35] Nonparametric approaches further estimate SFDs from high-resolution trajectory data, revealing that variance increases with density due to amplified interactions in congested regimes.^[36] In calibration, stochastic models parameterize percentile curves for flow-density scatter, enabling percentile-specific predictions that align with empirical histograms from loop detectors, where flow at a given density follows a skewed distribution peaking near the deterministic maximum but with heavy tails for low-flow states.^[37] Such formulations have been validated on freeway datasets, showing that stochastic perturbations in car-following rules reproduce observed fluctuations without assuming equilibrium homogeneity.^[38] Non-equilibrium extensions address limitations of steady-state assumptions by modeling transient dynamics, hysteresis, and phase transitions, where traffic states deviate from equilibrium due to instabilities like stop-and-go waves or capacity drops. These models predict that the flow-density relation is path-dependent, with congested branches not reverting instantly to free-flow upon perturbation removal, as evidenced in bottleneck simulations exhibiting lower flows during recovery than buildup phases.^[39] Continuum theories derive non-equilibrium equations from empirical velocity profiles, incorporating relaxation terms that delay adjustment to equilibrium, thus capturing spatiotemporal evolution absent in LWR models.^[40] Kerner's three-phase theory posits distinct phases—free flow, synchronized flow, and wide moving jams—challenging single-curve FDs by arguing that synchronized flow emerges metastably at densities below jam formation, with flow reductions not explained by equilibrium but by collective velocity oscillations. This framework, derived from German Autobahn data in the 1990s, uses state diagrams to depict transitions, where synchronized flow scatters below the equilibrium curve due to non-local interactions, though critics note it requires rejecting universal FD validity in favor of empirical phase boundaries.^[41] Recent validations extend this to three-dimensional representations, confirming hysteresis loops in urban networks where non-equilibrium states persist longer than predicted by two-phase models.^[42] Risk-based non-equilibrium models further integrate driver anticipation, yielding asymmetric diagrams with steeper congested slopes reflective of behavioral adaptations during transients.^[43]

Empirical Foundations

Data Collection and Measurement Techniques

Inductive loop detectors, embedded in the pavement, represent the most widespread traditional method for collecting traffic data used in fundamental diagram estimation, capturing vehicle passages to compute flow rates (vehicles per hour) directly from count frequencies over short intervals, typically 20-30 seconds.^[6] Dual-loop configurations, spaced 1-2 meters apart, additionally enable speed measurement by dividing the inter-loop distance by passage time differences, with reported accuracies exceeding 95% for individual vehicle speeds under free-flow conditions but degrading to 80-90% in congested regimes due to queue detection errors.^[44] Occupancy, the fraction of time the loop is activated, serves as a proxy for density when calibrated against known vehicle lengths, yielding estimates via k \approx \frac{\text{occupancy}}{\tau \cdot l}, where \tau is the detection interval and l is average vehicle length, though this introduces biases from vehicle heterogeneity.^[6] Pneumatic road tubes and magnetic sensors offer alternatives for temporary or low-cost deployments, with road tubes detecting axle passages via air pressure changes to derive flow and class-specific speeds, achieving count accuracies of 90-98% on low-volume roads but underperforming on high-speed arterials due to multi-axle misclassification.^[6] Video-based systems, employing machine vision algorithms, provide non-intrusive spatial coverage over defined zones, tracking vehicle trajectories to directly compute density as vehicles per unit length and flow via entrance-exit counts, with detection rates above 95% in daylight but dropping below 85% at night without infrared enhancements.^[6] These methods facilitate ground-truth validation of loop data, as demonstrated in freeway studies where video corrected loop-derived densities by 10-20% during breakdowns.^[44] Emerging techniques leverage probe vehicles equipped with GPS, contributing 1-5% penetration rates to estimate average speeds and densities via Edie's generalized definitions over network subsections: flow \bar{q} = \frac{\sum d_i(B)}{nTL}, density \bar{k} = \frac{\sum t_i(A)}{nTL}, and speed \bar{v} = \bar{q}/\bar{k}, where t_i(A) and d_i(B) are time spent and distance traveled by vehicle i in subarea A or B, aggregated over n vehicles, time T, and length L.^[45] Such data reduce point-measurement biases but require trajectory reconstruction, with accuracy improving to sub-5% error in speeds when fusing with infrastructure sensors.^[46] Radar and LiDAR sensors, mounted overhead or roadside, deliver volumetric detection for multi-lane coverage, measuring speeds via Doppler shifts with 98% accuracy up to 200 km/h and densities from point clouds, though susceptible to weather-induced signal attenuation.^[6] Historical data collection, as in Greenshields' 1934 study on rural highways, relied on manual timing of vehicle passages and photographic speed measurements, yielding pioneering flow-density relations from aggregated counts over 1-2 km sections with densities up to 20 veh/km.^[2] Contemporary aggregation for fundamental diagrams often employs 5-15 minute intervals to smooth fluctuations, balancing resolution against equilibrium assumptions, while multi-source fusion—e.g., loops with Bluetooth re-identification for origin-destination flows—enhances robustness against single-method failures like loop failures (occurring in 5-10% of installations annually).^[6]^[2]

Observed Patterns, Scatter, and Variability

Empirical data from loop detectors and trajectory measurements consistently depict the flow-density fundamental diagram as an inverted U-shaped curve, with flow rising linearly in the free-flow regime before reaching a maximum capacity and then decreasing toward zero at jam density. For instance, studies using aerial photogrammetry on U.S. freeways like I-71 observed maximum flows around 1,800 to 3,000 vehicles per hour per lane, transitioning abruptly into congested states with hysteresis loops indicating path-dependent recovery.^[2] Similarly, California Performance Measurement System (PeMS) data from urban freeways exhibit capacities of approximately 2,000–2,500 vehicles per hour per lane, with jam densities near 180–200 vehicles per kilometer per lane, though these parameters vary by roadway geometry and vehicle composition.^[26] Scatter in these diagrams is prominent, with data points deviating substantially from smooth theoretical curves, especially in congested regimes where variance in flow can exceed 20–30% of mean values. Analyses of Next Generation Simulation (NGSIM) trajectory data and UK M25 motorway loop detector records reveal wide dispersion attributable to stochastic driver behaviors, such as varying headways and acceleration responses, leading to probabilistic breakdowns rather than deterministic transitions.^[2] In high-density states, the joint probability density function of flow and density shows bimodality, with peaks shifting under variable speed limits—e.g., high-density modes from ~90 veh/km at 40 mph to ~45 veh/km at 70 mph—resulting in standard deviations peaking around intermediate speeds like 50 mph in low-density clusters.^[47] This variability stems primarily from transient non-equilibrium conditions, measurement inaccuracies in inductive loops during platooning, and external perturbations like incidents or weather, which amplify velocity variance as densities rise. Empirical fits to extensive datasets, including over 64 motorway links, confirm that triangular models like Daganzo-Newell capture patterns best but still require stochastic extensions to account for observed scatter, as deterministic formulations underpredict dispersion in jammed flows where standard deviations approach zero only near full stoppage.^[47]^[2] Heterogeneity in mixed traffic further exacerbates scatter through inter-vehicle interactions, with congested regimes displaying wider spreads due to platoon formation and dissolution.^[48] Overall, such patterns underscore the limitations of equilibrium assumptions, necessitating probabilistic models for accurate representation of real-world traffic dynamics.

Macroscopic Fundamental Diagram

Definition and Network Aggregation

The macroscopic fundamental diagram (MFD) delineates the relationship between the space-averaged density and space-averaged production (flow) across an entire urban road network, conceptualized as a single storage reservoir with internally circulating vehicles. Unlike microscopic fundamental diagrams for individual links, the MFD aggregates data to reveal a stable, low-scatter, unimodal curve: production rises with density up to a network-wide capacity maximum, beyond which congestion-induced spillbacks and inefficiencies cause production to decline. This framework emerged from empirical analyses of loop detector data in San Francisco, where link-level scatter diminished markedly at the network scale, as documented by Geroliminis and Daganzo in 2007.^[49] The approach posits that accumulation within the network—rather than boundary inflows—drives macroscopic performance, enabling simplified modeling for large-scale dynamics.^[50] Network aggregation for MFD construction entails computing macroscopic variables from microscopic link measurements, typically via length-weighted space-means to ensure consistency with fluid-like assumptions. Average density \bar{k} is the total vehicle accumulation A (summed across links) divided by total centerline road length L: \bar{k} = A / L (vehicles per km). Average production \bar{q}, representing network output in vehicle-km per hour per km, is total production P (aggregate vehicle-km traveled per hour) divided by L: \bar{q} = P / L. Production P derives from integrating link flows q_i over lengths l_i and time, often using Edie's generalized definitions for bounded regions: P = \frac{1}{T} \iint_B q(x,t) \, dx \, dt / L, where T is the observation interval and B the network boundary.^[50] ^[51] This aggregation presupposes homogeneous congestion distribution, well-mixed vehicle trips, and adequate sensor coverage (e.g., 10-30% of links in empirical studies like Zurich's, yielding reproducible curves via the law of large numbers). Deviations arise from uneven spatial loading or hysteresis, but validation across diverse networks confirms robustness under steady-state conditions. Loop detectors provide instantaneous link densities and flows for time-aggregation into 5-15 minute intervals, filtering transients to isolate equilibrium points for MFD fitting.^[52] ^[50] In heterogeneous cases, subarea partitioning or dynamic binning refines estimates, though over-aggregation can mask local bottlenecks.^[53]

Derivation from Microscopic Relations

The macroscopic fundamental diagram (MFD) emerges from microscopic traffic models through the aggregation of individual vehicle trajectories into network-wide averages, often using Edie's generalized definitions for space-time regions covering the entire network. In these definitions, the average density \bar{k} is the total time-integrated vehicle accumulation divided by the product of observation time T and total network length L, equivalent to the total number of vehicles n divided by L under uniform spatial distribution: \bar{k} = n / L.^[1] The average flow \bar{q} is the total distance traveled by all vehicles divided by T L, and the average speed \bar{v} is the ratio of total distance traveled to total vehicle-time spent, yielding \bar{v} = \bar{q} / \bar{k}.^[1] Microscopic car-following models, where each vehicle's acceleration depends on relative speed and spacing to its leader (e.g., intelligent driver model with parameters for desired speed, time headway, and acceleration), produce these aggregates when simulated over connected links; steady-state solutions assume constant headways inversely related to local density, leading to a network \bar{v}(\bar{k}) via mean-field averaging assuming well-mixed traffic.^[54] ![{\displaystyle {\bar {q}}={\frac {\sum {k=1}^{n}d{i}B}{nTL}}}}[center] In urban networks with signalized intersections, microscopic queuing at nodes dominates the derivation. Vehicles arrive uniformly at rate A_i to intersection i with outflow capacity \hat{Q}_i, yielding utilization u_i = A_i / \hat{Q}_i < 1. Green time fractions f_i satisfy f_i = (1 + \delta_i) u_i with excess capacity \delta_i > 0, and average delay T_{\mathrm{av},i} follows from deterministic queuing theory: T_{\mathrm{av},i} = \frac{(1 - f_i)^2 T_{\mathrm{cyc}}}{2(1 - u_i)}, adjusted for lost times \tau_j and cycle T_{\mathrm{cyc}}. The resulting link density \rho_{\mathrm{av},i} = (u_i \hat{Q}_i T_{\mathrm{av},i}) / L_i, aggregated over the network, produces a flow-density curve peaking at moderate \bar{k} due to delay-induced congestion, approximating a parabolic MFD shape \bar{q} \approx \bar{q}_{\max} (1 - (\bar{k}/\bar{k}_{\jam})^2).^[55] This microscopic foundation explains the scatter in empirical MFDs as arising from stochastic arrivals and signal variability, rather than assuming equilibrium homogeneity.^[56] ![{\displaystyle \tau ={\frac {d}{\bar {v}}}={\frac {nd}{MFDnL}}}}[inline] Extensions incorporate heterogeneous drivers or non-following behaviors (e.g., lane-changing), where microscopic simulations reveal MFD robustness for grid-like topologies but sensitivity to routing and boundary effects; for instance, in cellular automaton models akin to Nagel-Schreckenberg, phase transitions from free to jammed flow at network scale yield the MFD via ergodic averaging over many realizations.^[57] These derivations underscore that the MFD's existence relies on sufficient network connectivity for traffic homogenization, without which microscopic heterogeneities prevent a unique \bar{q}(\bar{k}).^[58]

Applications in Urban Traffic Management

The macroscopic fundamental diagram (MFD) facilitates urban traffic management by providing a network-level relationship between accumulation (vehicle count) and production (total trip completion rate), enabling aggregate control strategies that optimize overall network performance rather than individual links.^[59] In congested urban areas, MFD-based approaches treat the network as a reservoir, with interventions designed to maintain accumulation below the critical density where production peaks, typically around 20-30% of maximum capacity depending on network topology and signal coordination.^[60] This aggregation simplifies real-time monitoring using data from loop detectors or GPS, avoiding the computational burden of microscopic simulations for large-scale cities.^[61] A key application is perimeter control, or gating, which dynamically restricts inflows at network boundaries to prevent spillover congestion. Model predictive control (MPC) frameworks using MFD dynamics have demonstrated up to 20-30% reductions in total travel time in simulated multi-region urban networks by enforcing user-equilibrium inflows that align with system-optimal production.^[62] For instance, in a homogeneous urban region divided into subnetworks, each with its own MFD, perimeter signals adjust green times based on upstream accumulation estimates, stabilizing the system against demand fluctuations observed in peak hours (e.g., 7-9 AM).^[63] Empirical validations from loop detector data in cities like Rome confirm that such controls enhance the observed MFD's capacity envelope, though hysteresis (path-dependent scatter) requires robust state estimation to avoid underutilization.^[64] MFD-informed adaptive signal control extends local optimizations to network scale, adjusting cycle lengths or offsets to shift operations toward the MFD's capacity point. Studies show that networks with higher adaptive signal penetration exhibit elevated congested-regime flows in their MFDs, attributing 10-15% throughput gains to reduced gridlock propagation compared to fixed-time systems.^[65] In hybrid setups integrating connected vehicles, MFDs guide real-time prioritization, with analytical models predicting capacity increases of 15-25% under 20-50% penetration rates of cooperative adaptive cruise control.^[66] Reinforcement learning variants, such as those combining MFD with deep deterministic policy gradients, have outperformed traditional MPC in multi-agent simulations by adapting to heterogeneous traffic, achieving convergence to optimal states within 15-30 minutes of peak demand.^[67] These applications extend to integrated demand management, where MFDs inform pricing or routing to balance spatial loads across urban zones. For example, boundary controls coupled with variable tolls maintain production 5-10% above baseline in oversaturated conditions, as validated in dynamic traffic assignment models.^[68] Limitations arise in heterogeneous networks, where topological effects (e.g., uneven link distributions) distort the MFD, necessitating region partitioning via clustering algorithms for accurate control.^[69] Overall, MFD deployment in systems like Zurich's perimeter experiments underscores its utility for scalable, data-driven interventions, though real-world efficacy depends on precise accumulation tracking amid sensor noise and non-stationary demand.^[70]

Limitations and Criticisms

Assumptions of Equilibrium and Homogeneity

The fundamental diagram of traffic flow relies on the equilibrium assumption, which posits that traffic states are stationary, with flow q, density k, and speed v maintaining consistent relationships over time at a fixed location, absent transients or perturbations.^[26] This steady-state condition underpins macroscopic models like the LWR framework, where the continuity equation governs conserved flow without time-varying instabilities.^[71] However, empirical observations reveal that traffic rarely achieves such equilibrium; driver responses to stimuli introduce delays and oscillations, resulting in dynamic nonequilibrium behaviors that scatter data points away from the idealized curve.^[40] For instance, breakdown phenomena involve propagating shock waves that violate stationarity, as densities and flows fluctuate rapidly post-disruption.^[8] Complementing equilibrium, the homogeneity assumption requires uniform driver behaviors, identical vehicle properties (e.g., lengths, accelerations), and consistent roadway features across the observed section, enabling a single deterministic relation like the linear speed-density function in Greenshields' model.^[1] This uniformity simplifies aggregation into a parabolic flow-density diagram but overlooks real-world variability, such as mixed fleets with trucks reducing capacity or heterogeneous reaction times amplifying instabilities.^[72] Data from naturalistic driving studies show that non-homogeneous conditions—prevalent in urban settings with diverse commuters—produce multimodal distributions in flow-density plots, undermining the assumption's validity and contributing to predictive errors in capacity estimation.^[73] Critics argue these assumptions foster over-idealization, as user equilibrium concepts further presume rational, informed agents under homogeneous constraints, which field data contradicts through observed irrational lane-changing and information asymmetries.^[74] While useful for baseline modeling, relaxing equilibrium and homogeneity via stochastic or microscopic extensions better captures hysteresis and scatter, though at computational cost.^[2] Empirical validations, such as those from loop detectors, confirm that violations manifest as hysteresis loops during congestion onset and recovery, where paths diverge from equilibrium predictions.^[75]

Empirical Discrepancies and Hysteresis Effects

Empirical observations of the flow-density relationship in traffic streams reveal significant scatter, particularly in the congested regime, where data points deviate substantially from the idealized smooth curves predicted by deterministic models such as the Greenshields or triangular fundamental diagrams. This scatter arises from factors including measurement inaccuracies in loop detectors, temporal non-stationarity due to fluctuating driver behaviors, and spatial heterogeneity in road conditions or vehicle types, leading to inconsistent capacity estimates across sites. For instance, analyses of freeway data show that the congested branch exhibits wide dispersion, with flow variations exceeding 20-30% at similar densities, attributable to probabilistic distributions in vehicle gaps and synchronization effects among vehicles.^[76]^[77] Hysteresis effects manifest as asymmetric paths in the flow-density plane, where the trajectory during the onset of congestion (increasing density) yields higher flows than during dissipation (decreasing density), forming observable loops. At the link level, this is evident in bottleneck scenarios, such as freeway merges, where traffic breakdown triggers a capacity drop of 10-20% below the pre-breakdown maximum flow, followed by lower recovery flows due to persistent platooning and speed oscillations. Empirical studies using trajectory data from instrumented vehicles confirm that driver adaptation and wave propagation amplify these discrepancies, with hysteresis widths correlating to the sharpness of density transitions.^[78]^[79] At the network scale, macroscopic fundamental diagrams (MFDs) display analogous hysteresis, driven by spatiotemporal congestion patterns: buildup often involves localized queues that aggregate inefficiently, while dissipation requires coordinated spillback resolution, resulting in lower average network flows for equivalent densities. Data from urban networks, such as those in Zurich or San Francisco, quantify hysteresis areas equivalent to 5-15% flow deficits during recovery phases, challenging the assumption of instantaneous equilibrium and highlighting causal dependencies on initial congestion nucleation sites. These effects underscore the limitations of static models, as predictive accuracy diminishes without accounting for path-dependence in real-time operations.^[80]^[81]^[82]

Debates on Universality and Predictive Accuracy

Empirical investigations have sparked debates on the universality of the traffic flow fundamental diagram, with proponents citing scaling laws observed in freeway data, such as power-law distributions of jam cluster sizes and temporal durations aligning with Kardar-Parisi-Zhang universality class exponents (α=1/2, β=1/3, z=3/2), derived from high-resolution vehicle trajectories on a 4.2-mile Tennessee segment in 2022.^[83] These findings suggest invariant critical behavior across free-flow and congested regimes, potentially enabling phase-transition-based predictions. However, counter-evidence highlights topology-dependent variations in macroscopic fundamental diagrams; analyses of 63 Hong Kong networks using taxi GPS and station data show free-flow speeds exponentially declining with junction density (e.g., from 53.9 km/h at zero junctions/km) and optimal densities inversely tied to normalized degree density, rendering diagrams network-specific rather than invariant.^[69] Traffic heterogeneity further challenges universality, as mixed vehicle compositions—prevalent in urban settings—yield deviated flow-density relations unfit for homogeneous models; empirical studies in such environments advocate adjusted diagrams incorporating penetration rates of connected or autonomous vehicles to capture stability and capacity shifts.^[84] Flow-density empirics from 74 days across 64 UK motorway sections reveal bimodal distributions, with high-density scatter amplified by variable speed limits (e.g., greater variability at 40 mph versus 70 mph), and clustering into three distinct relationship types, underscoring location-specific parameters over a singular universal curve.^[47] Predictive accuracy of fundamental diagrams remains contested due to equilibrium assumptions clashing with transient dynamics; hysteresis effects, empirically documented in freeway networks, produce asymmetric loops wherein flows exceed dissipation-phase values for identical densities during congestion onset, as observed in loop detector data violating single-valued relations.^[80] Substantial data scatter from non-stationarity and interventions like speed controls erodes deterministic fidelity, with model fittings (e.g., via Akaike Information Criterion) exposing biases and inconsistent performance between continuous and interrupted flows, prompting hybrid Markov-LSTM frameworks for improved forecasting in heterogeneous conditions.^[27]^[85] These limitations imply that while diagrams inform capacity estimates, their standalone use overpredicts stability in variable real-world scenarios, favoring data-augmented extensions for operational reliability.

Applications and Extensions

Traffic Simulation and Control

The fundamental diagram underpins macroscopic traffic simulation models, notably the Cell Transmission Model (CTM), which discretizes roadways into uniform-length cells and computes inter-cell flows using the diagram's flow-density relationship to enforce conservation laws and capacity constraints. Developed by Carlos Daganzo in 1994, the CTM approximates the fundamental diagram with a triangular shape—featuring free-flow speed up to critical density and backward wave propagation thereafter—to determine sending and receiving flows, enabling dynamic simulation of congestion propagation without microscopic vehicle tracking.^[86]^[87] This approach has been extended to integrate with network-wide models, such as combining CTM for links with the macroscopic fundamental diagram (MFD) for urban areas, facilitating scalable simulations of large-scale traffic dynamics.^[86] In traffic control applications, the MFD extends the link-level fundamental diagram to aggregate network production (total flow) against accumulation (total density), enabling strategies like perimeter control to regulate boundary inflows and sustain operations near maximum throughput. Perimeter control, formalized in studies from the mid-2000s onward, uses real-time density estimates to adjust gating signals at network edges, preventing spillover into hyper-congested states by maintaining accumulation below critical levels, as demonstrated in empirical validations on cities like San Francisco and Yokohama.^[88]^[69] Such controls leverage the MFD's unimodal shape—rising to a capacity plateau then declining—to optimize global performance, with simulations showing reductions in total travel time by 10-20% under adaptive implementations compared to isolated signal timing.^[89] Hybrid simulations incorporating the fundamental diagram also support evaluation of connected and autonomous vehicles (CAVs), where calibrated diagrams inform lane-changing algorithms and flow predictions in mixed fleets, addressing asymmetries in information and behavior. For instance, CTM variants with piecewise linear fundamental diagrams have been used to model CAV penetration rates up to 50%, revealing capacity increases of 15-30% due to stabilized flow-density relations under cooperative maneuvers.^[66] These applications underscore the diagram's role in bridging empirical data calibration—often from loop detectors or probe vehicles—with predictive control, though real-world deployment requires hysteresis-aware adjustments to account for non-equilibrium scattering.^[84]

Planning and Policy Implications

The macroscopic fundamental diagram (MFD) enables urban planners to estimate the maximum sustainable throughput of a road network, typically around 15-20% of total link capacities depending on topology and signal timing, guiding decisions on infrastructure expansion versus demand management. By relating network-wide density to flow, the MFD highlights that exceeding critical densities leads to disproportionate congestion, informing policies to cap vehicle accumulation through access restrictions rather than indefinite road widening, which empirical studies show fails to resolve hypercongestion in dense cities.^[90]^[91] In traffic policy, MFD-based perimeter control regulates boundary inflows to maintain subcritical densities, maximizing network production by up to 10-15% in simulations of heterogeneous urban areas, as demonstrated in field trials and models from cities like Yokohama. This approach outperforms localized signal optimization by treating the network as a single reservoir, with real-time density feedback from sensors enabling adaptive gating, such as dynamic tolls or reversible lanes at cordons.^[92]^[93] For congestion pricing, MFD integration supports dynamic cordon schemes that internalize externalities by charging based on network state, shifting user equilibrium toward system optimum and reducing total travel time by 20-30% in multi-zone models, though implementation requires robust data to avoid hysteresis effects where scatter in observed MFDs undermines pricing accuracy. Policies leveraging MFD thus prioritize multi-modal integration, such as prioritizing buses in saturated networks, over car-centric expansions, aligning with causal evidence that network topology and homogeneity dominate capacity more than raw lane miles.^[94]^[95]^[69]

Recent Developments

Data-Driven and Hybrid Models

Data-driven models for the fundamental diagram (FD) of traffic flow utilize empirical datasets from loop detectors, probe vehicles, or trajectory data to estimate relationships between flow, density, and speed without relying solely on predefined parametric forms such as the Greenshields or triangular models. These approaches often employ machine learning techniques to fit non-analytical curves that capture observed scatter in real-world data, respecting constraints like flow equaling density times speed. For instance, a 2022 method derives a non-analytical FD by optimizing fits to empirical observations while enforcing physical consistency across traffic variables.^[96] Similarly, a 2025 framework automates FD construction from large-scale trajectory datasets by aggregating virtual detector data at 100-meter intervals, enabling scalable empirical derivation for highways.^[75] Hybrid models integrate data-driven elements with physics-based FD representations to enhance interpretability, prediction accuracy, and adherence to causal traffic dynamics, addressing limitations of purely black-box machine learning in handling non-stationary or heterogeneous conditions. A prominent example is the FD-Markov-LSTM model introduced in 2024, which stepwise combines a static FD for state estimation, Markov chains for transition modeling, and long short-term memory networks for temporal forecasting, outperforming standalone deep learning in traffic flow prediction on benchmark datasets.^[85] Another hybrid approach, the network macroscopic fundamental diagram-informed graph neural network (NMFD-GNN) from 2024, embeds FD-derived physical priors into graph learning to model network-wide flow-density relations, improving generalization across urban links by fusing empirical data with structural constraints.^[97] These models have demonstrated utility in mixed traffic scenarios, such as human-automated vehicle interactions, where hybrid FDs blend deterministic flow-density curves with stochastic data-driven adjustments to account for varying penetration rates of connected vehicles. Empirical validations, often using datasets like California's PeMS, show reduced prediction errors compared to traditional macroscopic models, particularly under congested or dynamic conditions, though challenges persist in computational scalability and data quality dependency.^[98]^[85]

Advances in Heterogeneous and Dynamic Traffic

Advances in modeling heterogeneous traffic have focused on multi-class vehicle compositions, such as mixtures of cars, two-wheelers, and autonomous vehicles (AVs), which deviate from the homogeneity assumption of classical fundamental diagrams (FDs). Heterogeneity manifests as increased scatter or spread in the flow-density relationship due to differing vehicle lengths, speeds, and behaviors; for instance, in mixed-autonomy traffic, this spread is quantified using optimal velocity models with driver attributes like desired speed and stochasticity, where AVs narrow the spread by 20% in calibration error compared to human-driven models through reduced variability in reaction times (e.g., AV τ=1.0-1.7 s vs. human 0.5 s) and desired gaps (5-15 m).^[99] Weighted inter-vehicle spacing models, incorporating quantified penetration rates (QPR) derived from socio-economic and network data, extend the macroscopic FD (MFD) to predict capacity gains of 35-59% at 25-35% AV penetration by 2037 in urban networks like Genoa, Italy, while accounting for AV levels (1-5) and traditional vehicles.^[100] Dynamic extensions address time-varying conditions, such as hysteresis and non-stationarity, where flow-density paths form loops due to congestion buildup and dissipation, as revealed by large-scale trajectory data analyses showing path-dependent equilibria rather than unique curves.^[51] Hybrid frameworks combine FDs for state identification (free-flow vs. congested) with Markov chains for transition probabilities and long short-term memory (LSTM) networks for residual predictions, yielding over 39% mean absolute error reduction and 35% root mean squared error improvement over benchmarks like ARIMA or standalone LSTM in dynamic urban flows.^[85] Self-adapting MFDs further incorporate temporal network heterogeneity by dynamically selecting effective road sections, enhancing stability in fluctuating conditions like peak-hour variations.^[101] These developments also integrate heterogeneity into emission MFDs (e-MFDs), where mixed fleets (e.g., gasoline vs. electric vehicles) are modeled with adjusted parameters for pollutant flows, improving accuracy by 10-15% in stability over prior versions through vehicle-type disaggregation.^[102] Three-dimensional MFDs for shared facilities, such as car-bicycle interactions, capture cross-modal effects, revealing capacity reductions of up to 20% from interference in heterogeneous urban streams.^[103] Overall, such models enable real-time control in diverse, evolving traffic, prioritizing empirical calibration from loop detectors and simulations over idealized assumptions.

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[91]
Understanding traffic capacity of urban networks - PMC
Nov 8, 2019 · Fortunately, the recently formulated Macroscopic Fundamental Diagram (MFD) provides new ways to systematically analyze urban traffic at the ...
[92]
Macroscopic fundamental diagram based perimeter control ...
This paper aims to integrate the MFD-based perimeter control (i.e., the behavior of a system manager) and the dynamic user equilibrium based route choice ...Missing: implications | Show results with:implications
[93]
The effect of signal settings on the macroscopic fundamental ...
Abstract: It has been proposed that a macroscopic fundamental diagram (MFD) can be used as input for perimeter control strategies.Missing: implications | Show results with:implications
[94]
Cordon Pricing Scheme Based on Macroscopic Fundamental ...
This study proposes an optimal pricing strategy that is based on the distance that users travel in a given cordon.
[95]
Modeling traffic dynamics in periphery-downtown urban networks ...
Modeling the dynamics of congestion in large urban networks using the macroscopic fundamental diagram: user equilibrium, system optimum, and pricing strategies ...
[96]
A Data-driven Approach for Estimating the Fundamental Diagram
A non-analytical fundamental diagram which best fits the empirical data and respects the relationships between traffic variables is developed in this paper. In ...
[97]
Network macroscopic fundamental diagram-informed graph learn
In this study, we introduce NMFD-GNN, a physics-informed machine learning method that fuses the network macroscopic fundamental diagram (NMFD) with the graph ...
[98]
A hybrid fundamental diagram for modeling mixed human and ...
A hybrid fundamental diagram for modeling mixed human and automated traffic flow ... Traffic flow fundamental diagram (FD) is viewed as the basis of traffic flow ...
[99]
[PDF] Characterizing Heterogeneity of Mixed-Autonomy Traffic - arXiv
The spread in the fundamental diagram means that the macroscopic traffic is heterogeneous, i.e., there is multiple flow corresponding to the same density.
[100]
Evaluation of macroscopic fundamental diagram characteristics for a ...
Mar 26, 2023 · This paper focuses on developing the macroscopic fundamental diagram for such heterogeneous traffic streams based on the quantified penetration ...
[101]
A novel self-adaption macroscopic fundamental diagram ...
Mar 1, 2023 · This paper proposes a self-adaption macroscopic fundamental diagram (SAMFD) considering network heterogeneity, which optimizes the effective road section ...
[102]
Heterogeneity Aware Emission Macroscopic Fundamental Diagram ...
This paper presents an advanced version of the emission macroscopic fundamental diagram (e-MFD) which improves the stability and accuracy of the previous model.
[103]
Three-dimensional macroscopic fundamental diagram for car and ...
Urban road facilities are typically shared by heterogeneous traffic, such as cars, buses, and bicycles. This paper investigates the car and bicycle traffic ...