The dynamics of traffic assignment and traffic control: A theoretical study

The paper considers traffic assignment, with traffic controls, in an increasingly dynamic way. First, a natural way of introducing the responsive policy, P_o, into steady state traffic assignment is presented. Then it is shown that natural stability results follow within a dynamical version of this static equilibrium model (still with a constant demand). We are able to obtain similar stability results when queues are explicitly allowed for, provided demand is constant. Finally we allow demand to vary with time; we consider the dynamic assignment problem with signal-settings now fixed. Here we assume that vehicles are very short and that deterministic queueing theory applies, and show that the time-dependent queueing delay at the bottleneck at the end of a link is a monotone function of the time-dependent input profile to the bottleneck. We have been unable to obtain results when dynamic demand and responsive signal control are combined.     

Microscopic Analysis of Cellular Automata Based Traffic Flow Models and an Improved Model

Abstract

A large number of cellular automata (CA) based traffic flow models have been proposed in the recent past. Often, the speed‐flow‐density relations obtained from these models are only presented and their apparent similarities with observed relations are cited as reasons for considering them as valid models of traffic flow. Hardly any attempt has been made to comprehensively study the microscopic properties (like time‐headway distribution, acceleration noise, stability in car‐following situations, etc.) of the simulated streams. This article proposes a framework for such evaluations. The article also presents the results from the evaluation of six existing CA‐based models. The results show that none of them satisfy all the properties. A new model proposed by the authors to overcome these shortcomings is briefly presented, and results supporting the improved performance of the proposed model are also provided.     

The treatment of traffic in the Dutch environmental outlooks: the role of science in policy making and policy evaluation

The Dutch National Institute of Public Health and Environmental Protection publishes Environmental Outlooks in which 25‐year projections are made. These Outlooks quantifying the environmental problems, form the scientific basis for Dutch environmental policy. Traffic and transport is one of the main sectors causing environmental problems. The emissions and energy use of all relevant categories (road traffic, non‐road traffic) are based on model simulations with models. This paper describes the main models used.

If present policy is implemented only a minority of the environmental targets will be met.

If a sustainable transport system for the Netherlands means a large reducton in CO₂ emissions and energy use after 2010 a stronger emphasis on both technical and non‐technical measures (such as land‐use planning combined with public transport improvements) for the period until 2010 is needed than proposed in the Second Transport Structure Plan, unless a sustainable energy source becomes available.     

A system dynamics approach to land use/transportation system performance modeling Part I: Methodology

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited due to data availability, the results indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling.     

A new probability statistical model for traffic noise prediction on free flow roads and control flow roads

A new traffic noise prediction approach based on a probability distribution model of vehicle noise emissions and achieved by Monte Carlo simulation is proposed in this paper. The probability distributions of the noise emissions of three types of vehicles are obtained using an experimental method. On this basis, a new probability statistical model for traffic noise prediction on free flow roads and control flow roads is established. The accuracy of the probability statistical model is verified by means of a comparison with the measured data, which has shown that the calculated results of L_eq, L₁₀, L₅₀, L₉₀, and the probability distribution of noise level occurrence agree well with the measurements. The results demonstrate that the new method can avoid the complicated process of traffic flow simulation but still maintain high accuracy for the traffic noise prediction.     

A multi‐class mean‐excess traffic equilibrium model with elastic demand

Recent empirical studies have revealed that travel time variability plays an important role in travelers' route choice decisions. To simultaneously account for both reliability and unreliability aspects of travel time variability, the concept of mean‐excess travel time (METT) was recently proposed as a new risk‐averse route choice criterion. In this paper, we extend the mean‐excess traffic equilibrium model to include heterogeneous risk‐aversion attitudes and elastic demand. Specifically, this model explicitly considers (1) multiple user classes with different risk‐aversions toward travel time variability when making route choice decisions under uncertainty and (2) the elasticity of travel demand as a function of METT when making travel choice decisions under uncertainty. This model is thus capable of modeling travelers' heterogeneous risk‐averse behaviors with both travel choice and route choice considerations. The proposed model is formulated as a variational inequality problem and solved via a route‐based algorithm using the modified alternating direction method. Numerical analyses are also provided to illustrate the features of the proposed model and the applicability of the solution algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

Visco‐elastic traffic flow model

To increase our understanding of the operations of traffic system, a visco‐elastic traffic model was proposed in analogy of non‐Newtonian fluid mechanics. The traffic model is based on mass and momentum conservations, and includes a constitutive relation similar to that of linear visco‐elastic fluids. The further inclusion of the elastic effect allows us to describe a high‐order traffic model more comprehensively because the use of relaxation time indicates that vehicle drivers adjust their time headway in a reasonable and safe range. The self‐organizing behaviour is described by introducing the effects of pressure and visco‐elasticity from the point of view in fluid mechanics. Both the viscosity and elasticity can be determined by using the relaxation time and the traffic sound speed. The sound speed can be approximately represented by the road operational parameters including the free‐flow speed, the jam density, and the density of saturation if the jam pressure in traffic flows is identical to the total pressure at the flow saturation point. A linear stability analysis showed that the traffic flow should be absolutely unstable for disturbances with short spatial wavelengths. There are two critical points of regime transition in traffic flows. The first point happens at the density of saturation, and the second point occurs at a density relating on the sound speed and the fundamental diagram of traffic flows. By using a triangular form flow–density relation, a numerical test based on the new model is carried out for congested traffic flows on a loop road without ramp effect. The numerical results are discussed and compared with the result of theoretical analysis and observation data of traffic flows. Copyright © 2011 John Wiley & Sons, Ltd.     

Development of a corrected average speed model for calculating carbon dioxide emissions per link unit on urban roads

The purpose of our study is to develop a “corrected average emission model,” i.e., an improved average speed model that accurately calculates CO₂ emissions on the road. When emissions from the central roads of a city are calculated, the existing average speed model only reflects the driving behavior of a vehicle that accelerates and decelerates due to signals and traffic. Therefore, we verified the accuracy of the average speed model, analyzed the causes of errors based on the instantaneous model utilizing second-by-second data from driving in a city center, and then developed a corrected model that can improve the accuracy. We collected GPS data from probe vehicles, and calculated and analyzed the average emissions and instantaneous emissions per link unit. Our results showed that the average speed model underestimated CO₂ emissions with an increase in acceleration and idle time for a speed range of 20 km/h and below, which is the speed range for traffic congestion. Based on these results, we analyzed the relationship between average emissions and instantaneous emissions according to the average speed per link unit, and we developed a model that performed better with an improved accuracy of calculated CO₂ emissions for 20 km/h and below.     

Quantifying errors in micro-scale emissions models using a case-study approach

To quantify the level of uncertainty attached to forecasts of CO₂ emissions, an analysis of errors is undertaken; looking at both errors inherent in the model structure and the uncertainties in the input data. Both error types are treated in relation to CO₂ emissions modelling using a case-study from Brisbane, Australia. To estimate input data uncertainty, an analysis of traffic conditions using Monte Carlo simulation is used. Model structure induced uncertainties are also quantified by statistical analysis for a number of traffic scenarios. To arrive at an optimal overall CO₂ prediction, the interaction between the two components is taken into account. Since a more complex model does not necessarily yield higher overall accuracy, a compromise solution is found. The results suggest that the CO₂ model used in the analysis produces low overall uncertainty under free flow traffic conditions. When average traffic speeds approach congested conditions, however, there are significant errors associated with emissions estimates.     

A microscopic analysis of speed deviation impacts on lane-changing behavior

Driving behavior models that capture drivers’ tactical maneuvering decisions in different traffic conditions are essential to microscopic traffic simulation systems. This paper focuses on a parameter that has a great impact on road users’ aggressive overtaking maneuvers and directly affects lane-changing models (an integral part of microscopic traffic simulation models), namely, speed deviation. The objective of this research is to investigate the impacts of speed deviation in terms of performance measures (delay time, network mean speed, and travel time duration) and the number of lane-change maneuvers using the Aimsun traffic simulator. Following calibration of the model for a section of urban highway in Tehran, this paper explores the sensitivity of lane-changing maneuvers during different speed deviations by conducting two types of test. Simulation results show that, by decreasing speed deviation, the number of lane changes reduces remarkably and so network safety increases, thus reducing travel time due to an increase in network mean speed.     

Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.     

Optimal location of advance warning for mandatory lane change near a two-lane highway off-ramp

Improper mandatory lane change (MLC) maneuvers in the vicinity of highway off-ramp will jeopardize traffic efficiency and safety. Providing an advance warning for lane change necessity is one of the efficient methods to perform systematic lane change management, which encourages smooth MLC maneuvers occurring at proper locations to mitigate the negative effects of MLC maneuvers on traffic flow nearby off-ramp. However, the state of the art indicates the lack of rigorous methods to optimally locate this advance warning so that the maximum benefit can be obtained. This research is motivated to address this gap. Specifically, the proposed approach considers that the area downstream of the advance warning includes two zones: (i) the green zone whose traffic ensures safe and smooth lane changes without speed deceleration (S-MLC); the start point of the green zone corresponding to the location of the advance warning; (ii) the yellow zone whose traffic leads to rush lane change maneuvers with speed deceleration (D-MLC). An optimization model is proposed to search for the optimal green and yellow zones. Traffic flow theory such as Greenshield model and shock wave analysis are used to analyze the impacts of the S-MLC and D-MLC maneuvers on the traffic delay. A grid search algorithm is applied to solve the optimization model. Numerical experiments conducted on the simulation model developed in Paramics 6.9.3 indicate that the proposed optimization model can identify the optimal location to set the advance MLC warning nearby an off-ramp so that the traffic delay resulting from lane change maneuvers is minimized, and the corresponding capacity drop and traffic oscillation can be efficiently mitigated. Moreover, the experiments validated the consistency of the green and yellow zones obtained in the simulation traffic flow and from the optimization model for a given optimally located MLC advance warning under various traffic regimes. The proposed approach can be implemented by roadside mobile warning facility or on-board GPS for human-driven vehicles, or embedded into lane change aid systems to serve connected and automated vehicles. Thus it will greatly contribute to both literature and engineering practice in lane change management.     

Norm approximation method for handling traffic count inconsistencies in path flow estimator

Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin–destination (O–D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original PFE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three L_p-PFE models for estimating consistent path flows and O–D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three L_p-PFE models. In addition, the proposed L_p-PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed.     

Combining driver response with cyclical queue optimisation over selected corridors

A case study located in Auckland, New Zealand, was used to quantify the magnitude of savings that may result if the SCATS adaptive traffic control system contains an explicitly combined queue estimation and offset adjustment on a cycle‐by‐cycle basis. A validated SATURN traffic model was used to evaluate five scenarios that represent the short‐run and long‐run efficiency gains resulting from progressive signal adaption with an objective of queue minimisation on the main corridors. Optimisation was applied both area‐wide, and on selected arterial corridors, using a combined split/offset optimisation routine with responsive driver behaviour to achieve a network‐wide and corridor‐specific efficiency gain. The modelling heuristic evaluates the efficiency of both the Equisat and P₀ optimisation policies that would mimic a more progressive adaption of signals under SCATS. Results for the long‐run area‐wide optimisation can produce network‐wide travel‐time savings in the order of 20% and a reduction in transient queues of 28% if only selected corridors are optimised, with a 5% reduction in journey time over an average 8‐min journey. Copyright © 2016 John Wiley & Sons, Ltd.     

Estimating link travel time functions for heterogeneous traffic flows on freeways

Oversized vehicles, such as trucks, significantly contribute to traffic delays on freeways. Heterogeneous traffic populations, that is, those consisting of multiple vehicles types, can exhibit more complicated travel behaviors in the operating speed and performance, depending on the traffic volume as well as the proportions of vehicle types. In order to estimate the component travel time functions for heterogeneous traffic flows on a freeway, this study develops a microscopic traffic‐simulation based four‐step method. A piecewise continuous function is proposed for each vehicle type and its parameters are estimated using the traffic data generated by a microscopic traffic simulation model. The illustrated experiments based on VISSIM model indicate that (i) in addition to traffic volume, traffic composition has significant influence on the travel time of vehicles and (ii) the respective estimations for travel time of heterogeneous flows could greatly improve their estimation accuracy. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimal speed detector density for the network with travel time information

In the expressway network, detectors are installed on the links for detecting the travel time information while the predicted travel time can be provided by the route guidance system (RGS). The speed detector density can be determined to influence flow distributions in such a way that the precision of the travel time information and the social cost of the speed detectors are optimized, provided that each driver chooses the minimum perceived travel time path in response to the predicted travel time information. In this paper, a bilevel programming model is proposed for the network with travel time information provided by the RGS. The lower-level problem is a probit-based traffic assignment model, while the upper-level problem is to determine the speed detector density that minimizes the measured travel time error variance as well as the social cost of the speed detectors. The sensitivity analysis based algorithm is proposed for the bilevel programming problem. Numerical examples are provided to illustrate the applications of the proposed model and of the solution algorithm.     

A system dynamics approach to land use / transportation system performance modeling Part II: Application

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model system consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is also examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited to some extent due to data availability, the results clearly indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling     

Real‐time identification of traffic conditions prone to injury and non‐injury crashes on freeways using genetic programming

This study applied the genetic programming (GP) model to identify traffic conditions prone to injury and property‐damage‐only (PDO) crashes in different traffic states on freeways. It was found that the traffic conditions prone to injury and PDO crashes can be classified into a high‐speed and a low‐speed traffic state. The random forest (RF) analyses were conducted to identify the contributing factors to injury and PDO crashes in these two traffic states. Four separate GP models were then developed to link the risks of injury and PDO crashes in two traffic states to the variables selected by the RF. An overall GP model was also developed for the combined dataset. It was found that the separate GP models that considered different traffic states and crash severity provided better predictive performance than the overall model, and the traffic flow variables that affected injury and PDO crashes were quite different across different traffic states. The proposed GP models were also compared with the traditional logistic regression models. The results suggested that the GP models outperformed the logistic regression models in terms of the prediction accuracy. More specifically, the GP models increased the prediction accuracy of injury crashes by 10.7% and 8.0% in the low‐speed and high‐speed traffic states. For PDO crashes, the GP models increased the prediction accuracy by 7.4% and 6.0% in the low‐speed and high‐speed traffic states. Copyright © 2016 John Wiley & Sons, Ltd.