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Highlights • Conversion into a route choice problem enables consideration of traffic variability. • Choice probability model can easily capture effects of non-recurring traffic events. • The model calibrated by massive trip data identifies trip anomalies dynamically. • Unintentional trip anomalies can be separated from the intentional ones effectively. Abstract Taxi service is an essential complement to public transport systems due to its convenience and availability. It often provides hundreds of millions of rides for urban travelers every year in cities across the world. At the same time, the number of trip-induced passenger complaints about trip anomalies (trips with anomalous trip length, time, fare, etc.) is also considerable. Hence, the taxi regulators impose harsh penalties on verified trip anomalies. The existing anomaly verification process is labor-intensive, and it does not consider the traffic variability as well as the passengers' perception of trip anomalies. Quite often the imprecise and unfair outputs are generated as a result. To tackle this issue, we propose a choice probability-based taxi trip anomaly detection model (ProbDetect) that considers the taxi drivers' route choice behavior as well as the traffic variability. We first generate a route choice set for each OD pair based on the massive taxi GPS trajectory data. Second, we assign each route with a choice probability derived from a cumulative multivariate probability over differences of generalized costs. Third, we distinguish the unintentional anomalies from the intentional anomalies using the expected and the realized choice probability. Lastly, the model is tested on 5000 OD pairs using 180 days of taxi GPS data in Shanghai, China. Three types of anomalies are detected as a result. Insights into the driver's route choice behavior are derived as well. [ABSTRACT FROM AUTHOR]

Highlights • Multiple congestion pricing and tolling strategies are investigated. • Different scenarios are considered for an AV-oriented world and an SAV-oriented world. • An agent-based model is adopted for simulations of the Austin, Texas region. • Effects of policies on traffic conditions and social welfare are measured. • More complex and advanced strategies perform better, depending on the studied scenario. Abstract The introduction of autonomous (self-driving) and shared autonomous vehicles (AVs and SAVs) will affect travel destinations and distances, mode choice, and congestion. From a traffic perspective, although some congestion reduction may be achieved (thanks to fewer crashes and tighter headways), car-trip frequencies and vehicle miles traveled (VMT) are likely to rise significantly, reducing the benefits of driverless vehicles. Congestion pricing (CP) and road tolls are key tools for moderating demand and incentivizing more socially and environmentally optimal travel choices. This work develops multiple CP and tolling strategies in alternative future scenarios, and investigates their effects on the Austin, Texas network conditions and traveler welfare, using the agent-based simulation model MATSim. Results suggest that, while all pricing strategies reduce congestion, their social welfare impacts differ in meaningful ways. More complex and advanced strategies perform better in terms of traffic conditions and traveler welfare, depending on the development of the mobility landscape of autonomous driving. The possibility to refund users by reinvesting toll revenues as traveler budgets plays a salient role in the overall efficiency of each CP strategy as well as in the public acceptability. [ABSTRACT FROM AUTHOR]

Highlights • We carried out a set of car-following experiment to investigate the car-following behavior under high speed situation. • Considerable heterogeneity for the same driver has been found. • The intra-driver heterogeneity was quantitatively depicted by a new index. • We propose an Enhanced Two Dimensional Intelligent Driver Model. • The new and three existing models were calibrated. • The enhanced model outperforms the three existing car-following models. Abstract To investigate the car-following behavior under high speed driving conditions, we performed a set of 11-car-platoon experiments on Hefei airport highway. The formation and growth of oscillations have been analyzed and compared with that in low speed situations. It was found that there is considerable heterogeneity for the same driver over different runs of the experiment. This intra-driver heterogeneity was quantitatively depicted by a new index and incorporated in an enhanced two-dimensional intelligent driver model. Using both the new high-speed and the previous low-speed experimental data, the new and three existing models were calibrated. Simulation results show that the enhanced model outperforms the three existing car-following models that do not take into account this intra-driver heterogeneity in reproducing the essential features of the traffic in the experiments. [ABSTRACT FROM AUTHOR]

Highlights • Model heterogeneous flows in the interdependent infrastructure system. • Consider partial capacity losses under facility disruption risks. • Formulate a non-linear integer programming model for the studied problem. • Draw insights into the features of the proposed model with numerical examples. • Reveal managerial insights into optimal location design and facility improvement. Abstract Due to the interdependency between multiple infrastructure systems, the performance of a facility may depend on the resources or supplies received from other facilities. However, cross-system interdependence has seldom been studied in the location design context, probably due to the lack of a concise model describing interdependence across heterogeneous systems. This paper proposes a new heterogeneous flow scheme to describe cross-system interdependence. This scheme has two features distinguished from existing models in describing an interdependent facility location problem. First, it is a simple linear model upon which a compact facility location model can be built. Secondly, it relaxes the need to maintain flow conservation between different systems and is suitable in describing heterogeneous systems that take in and output different resources or services. Built on this scheme, this paper proposes a reliable location design model for a nexus of interdependent infrastructure systems. This model aims to locate the optimal facility locations in multiple heterogeneous systems to balance the tradeoff between the facility investment and the expected nexus operation performance. Different from other reliable facility location models, this expected performance captures interdependence among heterogeneous systems due to the resource input-output relationships. The consideration of continuous partial capacity losses complements the reliable location literature that mainly focuses on binary disruptions. Two numerical examples are conducted for investigating features and applications of the proposed model. The results indicate that with a standard off-the-shelf integer programming solver, the proposed model is able to solve optimal facility location design for problem instances of realistic scales to the near-optimum solutions with optimality gap assurance. Sensitivity analyses of key parameters indicate that improving facility capacity and reducing interdependency between systems can mitigate impacts of facility capacity losses and reduce the overall system cost. [ABSTRACT FROM AUTHOR]

Highlights • Estimating historical traffic volumes between sparsely-located traffic sensors is addressed. • Neural network yields 24% more accurate results than the state of the practice approach. • Value of vehicle probe data in estimating historical traffic volumes is quantified. Abstract This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors. [ABSTRACT FROM AUTHOR]

This review discusses some issues related to the use of simulation in transportation analysis. Potential pitfalls are identified and discussed. An overview of some methods relevant to the use of an advanced simulation tool in an optimization context is also provided. [ABSTRACT FROM AUTHOR]

More and more commuters are beginning to favour public transportation. Fast and convenient park and ride (PnR) services provided by public transportation authorities are the result of changes of household demographics and household, increasing fuel prices and a focus on environmental sustainability. However, lack of parking spaces in PnR facilities creates a major bottleneck to this service. The aim of this research is to develop a location-based service (LBS) application to help PnR users choose the best train station to use to reach their destination using a multicriteria decision making model. A fuzzy logic method is used to estimate parking availability when a user is estimated to arrive at a PnR facility. Two surveys are conducted to collect traffic flow, travel behaviour and service quality data at four selected Perth Western Australia train stations. With the proposed approach and survey data, a prototype of LBS application, Station Finder, was developed using the Android SDK 4.0 and Google API 16. This application is a useful and practical tool to save travel cost and time of PnR users’. [ABSTRACT FROM AUTHOR]

The evacuation operations problem aims to avoid or mitigate the potential loss of life in a region threatened or affected by a disaster. It is shaped to a large extent by the evolution of evacuation traffic resulting from the demand–supply interactions of the associated transportation network. Information-based control is a strategic tool for evacuation traffic operations as it can enable greater access to the affected population and more effective response. However, comparatively few studies have focused on the implementation of information-based control in evacuation operations. This study develops a control module for evacuation operations centered on addressing the demand–supply interactions by using behavior-consistent information strategies. These strategies incorporate the likely responses of evacuees to the information provided in the determination of route guidance information. The control module works as an iterative computational process involving an evacuee route choice model and a control model of information strategies to determine the route guidance information to direct evacuation traffic so as to approach a desired network traffic flow pattern. The problem is formulated as a fuzzy logic based optimization framework to explicitly incorporate practical concerns related to information dissemination characteristics and social equity in evacuation operations. Numerical experiments highlight the importance of accounting for the demand–supply interactions, as the use of behavior-consistent information strategies can lead evacuee route choices to approach the operator-desired proportions corresponding to the desired traffic pattern. The results also indicate that while a behavior-consistent information strategy can be effective, gaps with the desired route proportions can exist due to the discrete nature of the linguistic messages and the real-world difficulty in accurately modeling evacuees’ actual route choice behavior. [ABSTRACT FROM AUTHOR]

Highlights: [•] Traffic dynamics on a ring road-based transportation network is considered. [•] Traffic dynamics is being analyzed based on real traffic flow data. [•] For the analysis, nonlinear methods of time series analysis are used. [•] Time series are tested for chaos by means of the novel 0–1 test for chaos. [•] Various traffic dynamics are distinguished on the basis of Lyapunov spectra. [ABSTRACT FROM AUTHOR]

Abstract: The problem of optimally locating sensors on a traffic network to measure flows has been object of growing interest in the past few years, due to its relevance in transportation systems. Different locations of sensors on the network can allow, indeed, the collection of data whose usage can be useful for traffic management and control purposes. Many different models have been proposed in the literature as well as corresponding solution approaches. The proposed existing models differ according to different criteria: (i) sensor types to be located on the network (e.g., counting sensors, image sensors, Automatic Vehicle Identification (AVI) readers), (ii) available a-priori information, and (iii) flows of interest (e.g., OD flows, route flows, link flows). The purpose of this paper is to review the existing contributions and to give a unifying picture of these models by categorizing them into two main problems: the Sensor Location Flow-Observability Problem and the Sensor Location Flow-Estimation Problem. For both the problems, we will describe the corresponding computational complexity and the existing results. After describing various models and identifying their advantages and limitations, we conclude with several promising directions for future research and discuss other classes of location problems that address different objectives than the ones reviewed in the paper. [Copyright &y& Elsevier]

Abstract: In advanced traffic signal control systems, plan transition is the process of switching from one timing plan to another to accommodate changes in traffic demand. In traditional coordinated system operation, whether plans are set by time of day or by traffic responsive plan selection, traffic flow can be severely disrupted by the sudden changes in traffic signal timing. Although various transition methods have been developed and refined, existing transition methods are not based on optimizing operational measures of effectiveness during plan transition periods. As the first step in remedying this situation, this paper presents a non-linear mathematical model that provides constrained delay minimization through incremental and simultaneous adjustments in offset and cycle length during plan transitions. According to the simulation results, currently used transition methods tend to assign an excessive amount of green time to the main street, resulting in additional side street delay without performance improvement for the total transportation network. By contrast, the proposed transition method showed measurable improvement in delay performance under a broad range of traffic and geometric conditions. [Copyright &y& Elsevier]

Abstract: This paper presents a schedule-based dynamic assignment model for transit networks, which takes into account congestion through explicit vehicle capacity constraints. The core of this assignment model is the use of a joint choice model for departure times, stops and runs that defines a space-time path in which users decide to leave at a given time, to access the network at a given stop and to board a given run to reach their destination. The assignment model is defined through a dynamic process approach in which the within-day network loading procedure allocates users on each transit run according to user choice and to the residual capacity of vehicles arriving at stops. The proposed model, albeit general, is specified for frequent users, who constitute a particularly congestion-sensitive class of users. Finally, an application to a real-size test network (part of the Naples transit network in southern Italy) is illustrated in order to test the proposed approach and show the ability of the modelling framework to assess congestion effects on transit networks. [Copyright &y& Elsevier]

Abstract: This paper aims to investigate the speed-flow relationship and drivers’ merging behavior in work zone merging areas. It first proposes lane-based speed-flow models, incorporating traffic conflicts among the lanes. It proceeds to develop a desired merging location model determining where drivers start to consider merging and a binary logit model that is applied to estimate the probabilities that drivers will merge into current adjacent gaps. A merging distance model is also proposed to find the 85th percentile of the merging distance. Finally, real work zone traffic data in Singapore are used to calibrate and evaluate the developed models. The findings show that the speed-flow relationship in the through lane is affected by the merge lane traffic under uncongested circumstances. Satisfactory results indicate that the merging behavioral models can competently predict drivers’ merging behavior and that the merging distance model could provide accurate information for traffic engineers to calculate the merge lane length. [Copyright &y& Elsevier]

Abstract: This paper presents a microscopic model for the study of operations at public transport stops, mainly bus stops and light rail transit stations, from the perspective of the traffic analysis. Firstly, a description of a public transport stop as a traffic mechanism is offered. Then the issue of stop capacity is reviewed, providing typical values of transit system capacities. The most wide-spread analytical model of stop capacity – the Highway Capacity Manual formula – and its components is discussed. Next, a microscopic simulation model as a tool for computing stop capacities and related impacts is described. Results of the calibration and validation of the model are also presented. Two applications are developed to illustrate the model capabilities. The first example deals with a light rail transit station and articulated trams; the second one consists of different classes of buses stopping at a bus stop. It can be concluded from these applications that the proposed model may provide more detailed information about stop operations than other existing approaches. [ABSTRACT FROM AUTHOR]

Abstract: A hybrid predictive control formulation based on evolutionary multi-objective optimization to optimize real-time operations of public transport systems is presented. The state space model includes bus position, expected load and arrival time at stops. The system is based on discrete events, and the possible operator control actions are: holding vehicles at stations and skipping some stations. The controller (operator) pursues the minimization of a dynamic objective function to generate better operational decisions under uncertain demand at bus stops. In this work, a multi-objective approach is conducted to include different goals in the optimization process that could be opposite. In this case, the optimization was defined in terms of two objectives: waiting time minimization on one side, and impact of the strategies on the other. A genetic algorithm method is proposed to solve the multi-objective dynamic problem. From the conducted experiments considering a single bus line corridor, we found that the two objectives are opposite but with a certain degree of overlapping, in the sense that in all cases both objectives significantly improve the level of service with respect to the open-loop scenario by regularizing the headways. On average, the observed trade-off validates the proposed multi-objective methodology for the studied system, allowing dynamically finding the pseudo-optimal Pareto front and making real-time decisions based on different optimization criteria reflected in the proposed objective function compounds. [Copyright &y& Elsevier]

Abstract: The effectiveness of California’s 1171mile High Occupancy Vehicle (HOV) system is assessed using peak hour traffic data from 700+ loop detector stations over many months. The study reaches the following conclusions. (1) HOV lanes are under-utilized: 81% of HOV detectors measure flows below 1400 vehicles per hour per lane (vphpl) during the PM peak hour. (2) Many HOV lanes experience degraded operations: 18% of all HOV miles during the AM peak hour and 32% during the PM peak hour have speeds below 45mph for more than 10% of weekdays. (3) HOV lanes suffer a 20% capacity penalty, achieving a maximum flow of 1600 vphpl at 45mph vs. maximum flow above 2000vphpl at 60mph in general purpose (GP) lanes. (4) HOV lanes offer small travel time savings. The mean savings over a random 10-mile route on an HOV lane vs. the adjacent GP lane is 1.7min and the median is 0.7min; however, HOV travel times are more reliable. (5) Travel time savings do not provide a statistically significant carpooling incentive. (6) A system with one HOV lane and three GP lanes carries the same number of persons per hour as a system with four GP lanes. (7) HOV lanes reduce overall congestion slightly only when the general purpose lanes are allowed to become congested. Despite these findings, HOV facilities can play a useful role in a well-managed freeway system in California. In particular, they can be useful if there is a significant number of buses or vanpools; as a 2-lane HOV/HOT facility, which eliminates capacity loss; and, with efficient metering, as a HOV/HOT bypass at the on-ramps. [Copyright &y& Elsevier]

Abstract: The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights in traffic dynamics in the temporal domain, this paper explored traffic patterns in higher-dimensional state spaces, where we attempted to map the one-dimensional traffic series into appropriate multidimensional spaces by Takens’ algorithm. After such a state space reconstruction, we then made use of the largest Lyapunov exponent to depict the rate of expansion or contraction of traffic state trajectories in the reconstructed spaces. The correlation dimension was further estimated to examine if the traffic state trajectories exhibited chaotic-like or stochastic-like motions. An empirical study using flow, speed, and occupancy time-series data as well as the speed-flow, speed-occupancy, and flow-occupancy paired data collected from dual-loop detectors on a freeway of Taiwan was conducted. The numerical results revealed that different nonlinear traffic patterns could emerge depending on the observed time-scale, history data and time-of-day. In addition, with consideration of sequential order and spatiotemporal features, more information about traffic dynamical evolution was extracted. [Copyright &y& Elsevier]

Abstract: In this work, we apply the iterative learning control approach to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The traffic density control problem is first formulated into an output tracking and disturbance rejection problem. Through rigorous analysis, it is shown that the iterative learning control method can effectively deal with this class of control problem and greatly improve the traffic response. Next, the iterative learning control is combined with error feedback in a complementary modular manner to achieve the output tracking and system robustness. The effectiveness of the new approach is further verified through case studies with intensive simulations. [Copyright &y& Elsevier]

Abstract: The relationships between instantaneous space-variables and (easily measurable) local time-variables in largely homogeneous and stationary traffic flow, as typically encountered in uninterrupted traffic conditions, are briefly reviewed. An elaborate analysis and microscopic simulation investigation is conducted with respect to the same relationships at signalized links with inherently strong traffic flow variations triggered by traffic signal switchings. A number of influencing factors when estimating space-variables from measured local time-variables is analysed and illustrated in detail. [Copyright &y& Elsevier]

Abstract: This paper describes and discusses the possibilities of steering the transportation planning process in the face of bounded rationality and unbounded uncertainty: (a) through the introduction of the concept of ‘systemicity’; (b) by expanding the spectrum of the existing planning paradigm currently in use; (c) by reducing complexity through the application of tests of adequacy, dependency, suitability, and adaptability; (d) through the introduction of soft systems thinking; and (e) by using ‘abductive’ in addition to deductive and inductive inferencing. It is concluded that the application of these strategies, adjustments, and tests to the existing planning procedure will hopefully enrich and strengthen our planning effort and make it more robust. [Copyright &y& Elsevier]

The aim of the paper is to evaluate the performance of a new strategy which is able to control dynamic route guidance (DRG) systems, mainly in urban road networks. The purpose of this strategy is to achieve dynamic user equilibrium in the network, even in abnormal network conditions, for example when there is an unexpected increase in traffic volume. It is based on feedback concept and it reacts to the traffic conditions observed in real time by adopting a decentralized structure.A series of experiments was performed, by means of a traffic micro-simulator, in a section of an urban road network. In the situations examined, the results seem to be quite positive. The analyses of the link level show that all of the various travel alternatives to reach the destination become more advantageous for users if DRG devices become more widespread among vehicles. In some cases we observe that the strategy succeeds in maintaining the possible alternatives in equilibrium conditions, by distributing users among the feasible turns. At no point in our investigations do we observe an unstable behaviour of the system, even when the number of vehicles fitted with a DRG device increases. [Copyright &y& Elsevier]

A number of mobile hosts might be densely distributed in an area caused by traffic congestions. The greater part of the mobile hosts will require commonly useful information, such as traffic information, parking information and other driving related information in such environment. It will be very effective to provide such information by using a common communication channel. In fixed networks, IP multicast is a well-known mechanism providing efficient point-to-multipoint data delivery, however it does not mention how to handle mobile hosts as a member of a multicast group.In this paper, we introduce a “BS multicast group” that is the set of base stations under which the mobile hosts belong to the same multicast group in a given area. Furthermore, we define new messages to control the BS multicast group based on the group management messages in IP multicast. Advanced Join mechanism, that manages the base stations in the BS multicast group by the control messages according to the movement of mobile hosts, eliminates lost packets during the movement of mobile hosts between the base stations. Evaluation results via the road model simulator show that the average rate of data reception of mobile hosts using the proposed mechanism is improved more than 17% and the time until a mobile host completes data reception is also improved more than 33%. Moreover, we recognize that it is indispensable to provide a data retransmission method to achieve more reliable data delivery to moving mobile hosts via the simulation results, and propose a simple data retransmission method called TOA (The order of arrival). By applying the TOA method to Advanced Join mechanism, the number of mobile hosts which complete the data reception is increased 6.9 times by the maximum. [Copyright &y& Elsevier]

This paper suggests to use market-based techniques for road traffic coordination. In particular, the question is addressed how to make it more attractive for traffic individuals to follow the suggestions of a coordination system. The presented techniques share a common principle: drivers participate in a voluntary pool of users. They pay into this pool or receive money from this pool according to their usage of the roads. The amount of payment is determined by a predefined electronic trading procedure in which each driver participates by an automated software unit representing the individual driver’s intentions. This paper compares three different trading mechanisms. The presented techniques are advantageous for the applications dynamic route assignment and individual road clearance. Compared to traditional traffic demand management systems, market-based techniques are expected to have a higher degree of flexibility and adaptability. Another advantage is that market-based techniques are independent of a prior set-up of traffic models and analyses. [Copyright &y& Elsevier]

Loop detectors are the preeminent vehicle detector for freeway traffic surveillance. Although single loops have been used for decades, debate continues on how to interpret the measurements. Many researchers have sought better estimates of velocity from single loops. The preceding work has emphasized techniques that use many samples of aggregate flow and occupancy to reduce the estimation error. Although rarely noted, these techniques effectively seek to reduce the bias due to long vehicles in measured occupancy. This paper presents a different approach, using a new aggregation methodology to estimate velocity and reduce the impact of long vehicles in the original traffic measurements. In contrast to conventional practice, the new estimate significantly reduces velocity estimation errors when it is not possible to control for a wide range of vehicle lengths. [Copyright &y& Elsevier]

This paper is concerned with the traffic flow stability/instability induced by a particular adaptive cruise control (ACC) policy, known as the “constant time headway (CTH) policy”. The control policy is analyzed for a circular highway using three different traffic models, namely a microscopic model, a spatially discrete model, and a spatially continuous model. It is shown that these three different modeling paradigms can result in different traffic stability properties unless the control policy and traffic dynamics are consistently abstracted for the different paradigms. The traffic dynamics will, however, be qualitatively consistent across the three modeling paradigms if a consistent biasing strategy is used to adapt the CTH policy to the various modeling frameworks. The biasing strategy determines whether the feedback quantity for use in the control, is taken colocatedly, downstream or upstream to the vehicle/section/highway location. For ACC vehicles equipped with forward looking sensors, the downstream biasing strategy should be used. In this case, the CTH policy induces exponentially stable traffic flow on a circular highway in all three modeling frameworks. It is also shown that traffic flow stability will be preserved for an open stretch highway if the entry and exit conditions are made to observe the downstream biasing strategy. [Copyright &y& Elsevier]