Impact of stop-and-go waves and lane changes on discharge rate in recovery flow

In an effort to uncover traffic conditions that trigger discharge rate reductions near active bottlenecks, this paper analyzed individual vehicle trajectories at a microscopic level and documented the findings. Based on an investigation of traffic flow involving diverse traffic situations, a driver’s tendency to take a significant headway after passing stop-and-go waves was identified as one of the influencing factors for discharge rate reduction. Conversely, the pattern of lane changers caused a transient increase in the discharge rate until the situation was relaxed after completing the lane-changing event. Although we observed a high flow from the incoming lane changers, the events ultimately caused adverse impacts on the traffic such that the disturbances generated stop-and-go waves. Based on this observation, we regard upstream lane changes and stop-and-go waves as the responsible factors for the decreased capacity at downstream of active bottlenecks. This empirical investigation also supports the resignation effect, the regressive effect, and the asymmetric behavioral models in differentiating acceleration and deceleration behaviors.     

Average time gaps in congested freeway flow

Characteristics of time gaps (that is, the time separation between the rear of the lead vehicle and the front of the following vehicle) in congested freeway flow provide an important link between microscopic and macroscopic traffic flow. Although individual time gaps are a microscopic phenomenon, average time gaps can easily be determined from commonly collected macroscopic traffic flow data. Data from San Diego freeways and the Queen Elizabeth Way in Ontario, Canada are analyzed to show that average time gaps in congested flow are essentially constant with respect to speed; that they vary considerably between lanes at a single location and, for the same lane, from site to site; that they display considerable scatter; and that at some sites there is a distinct increase in average time gaps in the median lane in the transition to congested flow but at others there is no change or a slight reduction. The variability of average time gaps is not easily explained, although differences in driver populations may partly explain differences among different sites. Hysteresis due to acceleration and deceleration does not appear to be an explanation for the high degree of scatter in average time gaps, since no positive correlation was found between speed changes and average time gaps.     

A high fidelity traffic simulation model based on cellular automata and car-following concepts

A high fidelity cell based traffic simulation model (CELLSIM) has been developed for simulation of high volume of traffic at the regional level. Straightforward algorithms and efficient use of computational resources make the model suitable for real time traffic simulation. The model formulation uses concepts of cellular automata (CA) and car-following (CF) models, but is more detailed than CA models and has realistic acceleration and deceleration models for vehicles. A simple dual-regime constant acceleration model has been used that requires minimal calculation compared to detailed acceleration models used in CF models. CELLSIM is simpler than most CF models; a simplified car-following logic has been developed using preferred time headway. Like CA models, integer values are used to make the model run faster. Space is discretized in small intervals and a new concept of percent space occupancy (SOC) is used to measure traffic congestion. CELLSIM performs well in congested and non-congested traffic conditions. It has been validated comprehensively at the macroscopic and microscopic levels using two sets of field data. Comparison of field data and CELLSIM for trajectories, average speed, density and volume show very close agreement. Statistical comparison of macroscopic parameters with other CF models indicates that CELLSIM performs as good as detailed CF models. Stability analyses conducted using mild and severe disturbances indicate that CELLSIM performs well under both conditions.     

Hysteresis in traffic flow revisited: An improved measurement method

This paper presents new insights on the hysteresis phenomenon in congested freeway traffic. It is found that existing theories based on different driver behavior for acceleration and deceleration are incomplete. The data suggests that one needs to consider aggressive and timid driver behavior as well. These findings are based on an improved method for measuring traffic flow variables from trajectory data consistently with kinematic wave theory.     

Towards a real-time microscopic emissions model

This article presents a new approach to microscopic road traffic exhaust emission modelling. The model described uses data from the SCOOT demand-responsive traffic control system implemented in over 170 cities across the world. Estimates of vehicle speed and classification are made using data from inductive detector loops located on every SCOOT link. This data feeds into a microscopic traffic model to enable enhanced modelling of the driving modes of vehicles (acceleration, deceleration, idling and cruising). Estimates of carbon monoxide emissions are made by applying emission factors from an extensive literature review. A critical appraisal of the development and validation of the model is given before the model is applied to a study of the impact of high emitting vehicles. The article concludes with a discussion of the requirements for the future development and benefits of the application of such a model.     

VISSIM/MOVES integration to investigate the effect of major key parameters on CO2 emissions

This paper looks at CO₂ emissions on limited access highways in a microscopic and stochastic environment using an optimal design approach. Estimating vehicle emissions based on second-by-second vehicle operation allows the integration of a microscopic traffic simulation model with the latest US Environmental Protection Agency’s mobile source emissions model to improve accuracy. A factorial experiment on a test bed prototype of the I-4 urban limited access highway corridor located in Orlando, Florida was conducted to identify the optimal settings for CO₂ emissions reduction and to develop a microscopic transportation emission prediction model. An exponentially decaying function towards a limiting value expressed in the freeway capacity is found to correlate with CO₂ emission rates. Moreover, speeds between 55 and 60 mph show emission rate reduction effect while maintaining up to 90% of the freeway’s capacity. The results show that speed has a significant impact on CO₂ emissions when detailed and microscopic analysis of vehicle operations of acceleration and deceleration are considered.     

Speed or spacing? Cumulative variables,and convolution of model errors and time in traffic flow models validation and calibration

This paper proves that in traffic flow model calibration and validation the cumulative sum of a variable has to be preferred to the variable itself as a measure of performance. As shown through analytical relationships, model residuals dynamics are preserved if discrepancy measures of a model against reality are calculated on a cumulative variable, rather than on the variable itself. Keeping memory of model residuals occurrence times is essential in traffic flow modelling where the ability of reproducing the dynamics of a phenomenon – as a bottleneck evolution or a vehicle deceleration profile – may count as much as the ability of reproducing its order of magnitude. According to the aforesaid finding, in a car-following models context, calibration on travelled space is more robust than calibration on speed or acceleration. Similarly in case of macroscopic traffic flow models validation and calibration, cumulative flows are to be preferred to flows. Actually, the findings above hold for any dynamic model.     

Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation

This paper examines the impact of having cooperative adaptive cruise control (CACC) embedded vehicles on traffic flow characteristics of a multilane highway system. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce traffic congestion resulting from the acceleration/deceleration of the operating vehicles. An agent-based microscopic traffic simulation model (Flexible Agent-based Simulator of Traffic) is designed specifically to examine the impact of these intelligent vehicles on traffic flow. The flow rate of cars, the travel time spent, and other metrics indicating the evolution of traffic congestion throughout the lifecycle of the model are analyzed. Different CACC penetration levels are studied. The results indicate a better traffic flow performance and higher capacity in the case of CACC penetration compared to the scenario without CACC-embedded vehicles.     

Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections

Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave. This paper examines the effect of traffic, vehicle and road characteristics on vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics. It demonstrates the relationships of traffic, vehicle and intersection characteristics with vehicular exhaust emissions and reviews the traffic flow and emission models. Most studies have found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions. The vehicular composition also affects emissions. These parameters can be quantified and incorporated into the emission models. There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions     

Network-wide traffic and environmental impacts of acceleration and deceleration among Eco-Driving Vehicles in different road configurations

Eco-Driving, a driver behaviour-based method, has featured in a number of national policy documents as part of CO₂ emission reduction or climate change strategies. This investigation comprises a detailed assessment of acceleration and deceleration in Eco-Driving Vehicles at different penetration levels in the vehicle fleet, under varying traffic composition and volume. The impacts of Eco-Driving on network-wide traffic and environmental performance at a number of speed-restricted road networks (30?km/h) is quantified using microsimulation. The results show that increasing levels of Eco-Driving in certain road networks result in significant environmental and traffic congestion detriments at the road network level in the presence of heavy traffic. Increases in CO₂ emissions of up to 18% were found. However, with the addition of vehicle-to-vehicle or vehicle-to-infrastructure communication technology which facilitates dynamic driving control on speed and acceleration/deceleration in vehicles, improvements in CO₂ emissions and traffic congestion are possible using Eco-Driving.     

Investigating empirical implications of hysteresis in day-to-day travel time variability

Day-to-day travel time variability plays a significant role in travel time reliability. Nowadays, travelers not only seek to minimize their travel time on average, but also value its variation. The variation in the mean and the variance of travel time (across days, for the same departure time) has not been thoroughly investigated. A temporary decrease in capacity (e.g. congestion caused by an active bottleneck) leads to a quite significant difference in the variance of travel time for congestion onset and offset periods. This phenomenon results in hysteresis loops where the departure time periods in congestion offset exhibit a higher travel time variance than the ones in congestion onset with the same mean travel time. The aim of this paper is to identify empirical implications that yield to the hysteresis phenomenon in day-to-day travel times. First, empirical hysteresis loop observations are provided from two different freeway sites. Second, we investigate the potential link with the hysteresis observed in traffic networks on macroscopic fundamental diagram (MFD). Third, we build a piecewise linear function that models the evolution of travel time within the day. This allows us to decompose the problem into its components, e.g. start time of congestion, peak travel time, etc. These components, along with their probability distribution functions, are employed in a Monte Carlo simulation model to investigate their partial effects on the existence of hysteresis. Correlation among critical variables is the most influential factor in this phenomenon, which should be further investigated regarding traffic flow and traffic equilibrium principles.     

Traffic flow on signalized streets

This paper considers a signalized street of uniform width and blocks of various lengths. Its signals are pretimed in an arbitrary pattern, and traffic on it behaves as per the kinematic-wave/variational theory with a triangular fundamental diagram. It is shown that the long run average flow on the street when the number of cars on the street (i.e. the street’s density) is held constant is given by the solution of a linear program (LP) with a finite number of variables and constraints. This defines a point on the street’s macroscopic fundamental diagram. For the homogeneous special case where the block lengths and signal timings are identical, all the LP constraints but one are redundant and the result has a closed form. In this case, the LP recipe matches and simplifies the so-called “method of cuts”. This establishes that the method of cuts is exact for homogeneous problems. However, in the more realistic inhomogeneous case the difference between the two methods can be arbitrarily large.The paper uses the LP method to obtain the macroscopic fundamental diagrams arising under four different traffic coordination schemes for streets with four different block length configurations. It is found that the best scheme depends on the prevailing density. Curiously, the popular scheme in which all the traffic green phases are started synchronously wins only in rare circumstances. Its performance is particularly underwhelming when the street’s blocks are long. The paper also presents density-aware numerical methods to optimize the signal offsets for 1-way and 2-way streets. For 1-way streets operated with a common cycle the method reduces to a simple graphical construction . In this case the resulting flow matches the flow that would arise if all of the street’s intersections except one with the shortest green phase had been eliminated.     

Hysteresis phenomena of a Macroscopic Fundamental Diagram in freeway networks

Observations of traffic pairs of flow vs. density or occupancy for individual locations in freeways or arterials are usually scattered about an underlying curve. Recent observations from empirical data in arterial networks showed that in some cases by aggregating the highly scattered plots of flow vs. density from individual loop detectors, the scatter almost disappears and well-defined macroscopic relations exist between space-mean network flow and network density. Despite these findings for the existence of well-defined relations with low scatter, these curves should not be universal. In this paper we investigate if well-defined macroscopic relations exist for freeway network systems, by analyzing real data from Minnesota’s freeways. We show that freeway network systems not only have curves with high scatter, but they also exhibit hysteresis phenomena, where higher network flows are observed for the same average network density in the onset and lower in the offset of congestion. The mechanisms of traffic hysteresis phenomena at the network level are analyzed in this paper and they have dissimilarities to the causes of the hysteresis phenomena at the micro/meso level. The explanation of the phenomenon is dual. The first reason is that there are different spatial and temporal distributions of congestion for the same level of average density. Another reason is the synchronized occurrence of transitions from individual detectors during the offset of the peak period, with points remain beneath the equilibrium curve. Both the hysteresis phenomenon and its causes are consistently observed for different spatial aggregations of the network.     

Theory of safety surrogates using vehicle trajectories in macroscopic and microscopic settings: Application to dynamic message signs controlled traffic at work zones

This paper presents a new mathematical framework for obtaining quantitative safety measure using macroscopic as well as microscopic traffic data. The safety surrogate obtained from the macroscopic data is in terms of analysis performed on vehicle trajectories obtained from the macroscopic data. This method of obtaining safety measure can be used for many different types of applications. The safety surrogate for the traffic dynamics are developed in terms of a new concept of Negative Speed Differentials (NSD) that involve a convolution of vehicle speed function obtained from vehicle trajectories and then performing the integration of the square of the output for its negative values. The framework is applicable to microscopic traffic dynamics as well where we can use car following models for microscopic dynamics or the LWR model for macroscopic dynamics. This paper then presents the use of this new safety surrogate on the development of a feedback control law for controlling traffic in work zones using Dynamic Message Signs. A hybrid dynamics model is used to represent the switching dynamics due to changing DMS messages. A feedback control design for choosing those messages is presented as well as a simple simulation example to show its application.     

Autonomous cars: The tension between occupant experience and intersection capacity

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks.In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation.On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda.This paper concludes with a brief discussion of research needs to advance this line of inquiry.     

Phase transition model of non-stationary traffic flow: Definition,properties and solution method

We consider the problem of modeling traffic phenomena at a macroscopic level. Increasing availability of streaming probe data allowing the observation of non-stationary traffic motivates the development of models capable of leveraging this information. We propose a phase transition model of non-stationary traffic in conservation form, capable of propagating joint measurements from fixed and mobile sensors, to model complex traffic phenomena such as hysteresis and phantom jams, and to account for forward propagation of information in congested traffic. The model is shown to reduce to the Lighthill–Whitham–Richards model within each traffic phase for the case of stationary states, and to have a physical mesoscopic interpretation in terms of drivers’ behavior. A corresponding discrete formulation appropriate for practical implementation is shown to provide accurate numerical solution to the proposed model. The performance of the model introduced is assessed on benchmark cases and on experimental vehicle trajectories from the NGSIM datasets.     

Do cooperative systems make drivers’ car-following behavior safer?

The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers’ car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.     

Within day rescheduling microsimulation combined with macrosimulated traffic

The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling at a large scale. The framework allows to explicitly model the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and for information going unnoticed; perception filters feed person specific short term predictions about the environment required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both actor behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules, again affecting the network via updated traffic demand. The first rescheduling mechanism that has been investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation is aimed to support re-timing, re-location and activity re-sequencing; re-routing at the level of the individual however, requires microscopic travel simulation.     

Macroscopic traffic flow propagation stability for adaptive cruise controlled vehicles

Traffic flow propagation stability is concerned about whether a traffic flow perturbation will propagate and form a traffic shockwave. In this paper, we discuss a general approach to the macroscopic traffic flow propagation stability for adaptive cruise controlled (ACC) vehicles. We present a macroscopic model with velocity saturation for traffic flow in which each individual vehicle is controlled by an adaptive cruise control spacing policy. A nonlinear traffic flow stability criterion is investigated using a wavefront expansion technique. Quantitative relationships between traffic flow stability and model parameters (such as traffic flow and speed, etc.) are derived for a generalized ACC traffic flow model. The newly derived stability results are in agreement with previously derived results that were obtained using both microscopic and macroscopic models with a constant time headway (CTH) policy. Moreover, the stability results derived in this paper provide sufficient and necessary conditions for ACC traffic flow stability and can be used to design other ACC spacing policies.