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Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.

In this paper we present two low cost, airborne sensor systems capable of long-term vehicle tracking. Based on the properties of the sensors, a method for automatic real-time, long-term tracking of individual vehicles is presented. This combines the detection and tracking of the vehicle in low frame rate image sequences and applies the lagged Cell Transmission Model (CTM) to handle longer tracking outages occurring in complex traffic situations, e.g. tunnels. The CTM model uses the traffic conditions in the proximities of the target vehicle and estimates its motion to predict the position where it reappears. The method is validated on an airborne image sequence acquired from a helicopter. Several reference vehicles are tracked within a range of 500m in a complex urban traffic situation. An artificial tracking outage of 240m is simulated, which is handled by the CTM. For this, all the vehicles in the close proximity are automatically detected and tracked to estimate the basic density-flow relations of the CTM model. Finally, the real and simulated trajectories of the reference vehicles in the outage are compared showing good correspondence also in congested traffic situations.

While agent-based modelling of traffic demand is gaining attention, a macroscopic dynamic network loading model may be beneficial, particularly in large-scale applications. We investigate the implications of coupling such models, with inclusion of en-route choices, for the modelling of links and the determination of turning fractions, yielding useful recommendations to help select an appropriate solution scheme of the macroscopic traffic flow theory and overcome other practical challenges specifically associated with the coupling of agent-based traffic demand and macroscopic traffic propagation.

We consider an extension of the macroscopic traffic model of Lighthill–Whitham and Richards to a multispecies traffic model. As has already been observed by Benzoni-Gavage and Colombo (Eur J Appl Math 14:587–612, 2003, [1]), the system of PDEs lacks strict hyperbolicity. We study the Riemann problem for the two species extension with focus on the values around the umbilic point, where the eigenvalues coalesce. For this purpose, we examine the behavior of the solutions around the critical point like it is done by Meltzer (Multispecies traffic models based on the Lighthill–Whitham and the Aw-Rascle model, 2016, [9]). Due to the difficulty of the umbilic point, we are not able to prove well-posedness analytically. But we provide an understanding of the systems properties via numerical experiments which leads to the conjecture that the Riemann problem has a unique solution not only near the umbilic point but also away from it in the set where it is defined.

Recent directives in France on sustainable development have driven local Authorities to the reduction of speed limits on urban highways. Consequently, Local Authorities are interested in a thorough evaluation of this measure and in the estimation of the potential impact upon traffic and emissions. In this paper, we undertake an a priori evaluation of speed limit reduction from 110 km/h to 90 km/h on the Lille motorway network, in France. We first provide an overview of key lessons learned from previous speed limit reduction experiences. Then, we present the methodology of the specific prior evaluation on the Lille network. In particular, we use a first order macroscopic traffic simulation tool for this a priori assessment. The model is first calibrated and then statistically validated using traffic data. This statistical validation is consistent with good practices in simulation. The evaluation output allows for a thorough understanding of the impacts on prevailing traffic conditions and air pollution. Additional scenarios are also considered in order to take into account driver behaviour on the non-compliance with speed limits. Besides the various limitations of the study, especially those related to its predictive aspects, we can conclude that the intended measure will have a positive impact on traffic and emissions. Nevertheless, the magnitude of this impact is largely variable, in particular in regards to driver compliance.

We investigate the relations between a macroscopic Lighthill-Whitham and Richards model and a microscopic follow-the-leader model for traffic flow. Solutions to the microscopic model are proved to tend to those to the macroscopic one in a sort of kinetic limit, i.e. as the number of individuals tends to +∞ while their total mass is constant. Based on this convergence result, we approximately compute the solutions to a conservation law by means of the integration of an ordinary differential system.

Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. Ph. D.

The assumption that the penalty for being early is less than that for being late was put forward by Vickrey (1963) who analyzed how commuters compare penalties in the form of schedule delay (due to peak hour congestion), against penalties in the form of reaching their destination (ahead or behind their desired time of arrival). This assumption has been tested by many researchers since then for various applications, especially in modeling congestion pricing (Arnott et al., 1990) where it is critical to understand the tradeoff between schedule delay and travel delay. Key findings are summarized in the second section of this paper. This research aims to test this hypothesis of earliness being less expensive than lateness using empirical data at different levels and across different regions. New methods to estimate the ratio of earliness to lateness for different types of datasets are developed, which could be used by agencies to implement control policies like congestion pricing or other schemes more accurately. Travel survey data from metropolitan areas provide individual travel patterns while loop detector data provide link level traffic flow data.