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Considering the significant increase in transportation (R. Abe, 2019) demand in the last few decades and its consequences on society and the environment, the development of a sustainable transport system is vital. The relevance of public transport networks is expanding as present-day environmental, financial and resource pressures bear upon cities, providing the impetus for new sustainable and economical modes of mobility. Promoting public transport and introducing connectivity and automation are crucial for effectuating a sustainable transport system. Buses can improve the network performance, but curbside bus stops (CBS) elevate the side friction which cause substantial delays to other vehicles, urging imprudent forced lane changes and affecting safety. The introduction of connectivity and automation in buses may improve performance along with safety, and this study attempts to investigate the repercussions of connected automated buses (CABs) in a mixed fleet with connected automated vehicles (CAVs), through a microsimulation modelling exercise. Results indicate that CABs can reduce network-level travel time by 7.9%, 6.4%, 2.1%, 1.3%, 1.8%, 1.2% and 2.27% for 0%, 10%, 30%, 50%, 70%, 90% and 100% CAV penetration, when compared to the network with Human-driven buses (HBs) with the same CAV penetration. Furthermore, CABs also improve the performance of link containing bus stops by reducing standstill times (timespan when buses have zero velocity on the bus stop link). Results exhibit that CABs can decrease standstill times by 12.5%, 13.3%, 12.6%, 15%, 12.9%, 16.7% and 19.9% for 0%, 10%, 30%, 50%, 70%, 90% and 100% CAV penetration, when compared to the network with HBs with identical CAV penetration. Additionally, the number of lane changes in these links were reduced by 28.2% at 100% CAV penetration, indicating synchronous driving and reduced forced lane changes. The crash rate indicates that critical events involving automated vehicle entities and Manual Vehicles (MV) can increase as the MVs cannot adapt promptly to automated driving behaviour. This is supported by the non- parametric distribution of post-encroachment time (PET), time- to-collision (TTC) and relative speed (DeltaS), indicating that the conflict/collision typology's severity can increase at intermediate CAV penetrations, but plunge significantly at 100% penetration when compared to the current scenario. Lastly, the limitation of tools in flagging conflicts involving CAVs is discussed, and future scrutiny is proposed.

The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lane-level traffic conditions and vehicles' lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads.

Vehicle speed is known as one of the most important parameters in the various goals for driving. Although the most critical goal of driving is safety, each driver can consider another secondary goal, such as reducing travel time, economical driving, green driving, passenger comfort, etc. In this paper, we propose a smartphone-based application that utilizes the cloud/fog computing service infrastructure in order to recommend speed according to primary safety goal and another secondary objective. Due to the strategic nature of the driving speed selection issue, we have modeled the problem as a game that the drivers are the players, and the speed of the vehicle is their strategy. We solve the proposed game via evolutionary dynamics with appropriate convergence time, and the resulting equilibrium profile is announced to the drivers as the recommended speed at specified time intervals. Finally, the experimental results obtained from the simulation of the proposed scheme confirm that the deviation from the proposed equilibrium velocity is not profitable for the offending player. Indeed, deviation from equilibrium conditions leads to the negative impacts on the formal evaluation parameters presented in this paper.

With the help of inter-vehicle communication (IVC), autonomous vehicles (AVs) can drive cooperatively, thus significantly improve road safety, traffic efficiency, and environmental sustainability. While substantial research has been conducted to investigate the efficiency of AVs in transportation systems, few attempts have been carried out to explore the lane-changing operations of AVs in multi-lane freeways, especially the dynamics of AVs at lane-drop bottlenecks. To this end, this paper aims to develop a cooperative space distribution method (CSDM) not only to increase the lane-drop bottleneck's throughput but also to equally distribute the AVs in the dropped lane to other lanes by efficiently coordinating the dynamics of AVs upstream of the bottleneck in a multi-lane freeway. More specifically, we propose a novel framework where the freeway (region of interest) is divided into three segments: i) platoon segment, ii) acceleration segment, and iii) merging segment. In the first segment, AVs travel together in the platoon to guarantee their safety; they will then speed up to attain the maximum velocity and reach the determined position in the second segment. Finally, these AVs change lanes in the last segment and pass through the bottleneck with maximum velocity and minimum gap (i.e., gap distance). Simulation results are tested to demonstrate the performance of the proposed method, where we show that the proposed framework can significantly decrease the average travel time of all vehicles.

The time-dependent shortest path and vehicle routing literature depends on realistic and reasonable test data for demonstration and performance evaluation. Despite the advancements in GPS and tracking technologies there is still lack and inaccessibility of publicly available real-world road networks with time-dependent arc costs and speeds. Since most of the time-dependent travel time layer generation models proposed for vehicle routing problems (VRPs) are mainly developed for synthetic networks, they cannot capture some realistic features of the real road networks and cannot be used in time-dependent shortest path problems (TDSPPs). In this paper, we first exploit spatial and temporal behavior of travel times using real life road network and speed data, and discuss the cases where the existing methods in the literature are not applicable. Then, we propose an enhanced method that is best fitted for TDSPP and time-dependent VRP (TDVRP). The proposed method can be implemented on both synthetic and real road networks. Finally, we apply our method to generate realistic speed data on Istanbul road network and demonstrate the applicability in TDSPP and TDVRP.

In high-frequency electric bus services, bus operators strive to increase the service regularity by minimizing the deviation between the planned and actual headways. In this pursue, bus operators apply corrective control strategies, such as bus holding(s) at control point stops. This study expands the traditional headway-based, bus holding models to cater for the planned arrival times of electric buses at the respective charging points. To this end, this study models - for the first time - the bus holding problem for electric buses considering the scheduled charging times in the objective function. Additionally, it introduces an analytic solution that can return an optimal holding decision in real-time. Our approach is tested using realistic data from bus line 15 in Amsterdam demonstrating a significant reduction of charging delays with only a marginal increase of the average passenger waiting times when compared to existing holding strategies. Its closed-form expression is suitable for real-time holding control and can be applied to obtain a reliable solution or perform stochastic optimization in the case of travel time uncertainty.

Changing lanes is a commonly-used technique for drivers to either overtake slow-moving cars or enter/exit highway ramps. Optional lane changes may save drivers travel time but increase the risk of collision with others. Drivers make such decisions based on experience and emotion rather than analysis, and thus may fail to select the best solution while in a dynamic state of flux. Unlike human drivers, autonomous vehicles can systematically analyze their surroundings and make real-time decisions accordingly. This paper develops a game theory-based lane-changing model by comparing two types of optimization methods. To realize our expectations, we need to first investigate the payoff function of drivers in discretionary lane-changing maneuvers and then quantify it in an equation of costs that trades-off safety and time-saving. After the evaluation for each alternative strategy combination, the results show that there exists a social gap in the discretionary lane-changing game. To deal with that problem, we provide some suggestions for future policy as well as autonomous vehicle controller designs, offering solutions to reduce the impact of disturbances and crashes caused by inappropriate lane changes, and also, inspire further research about more complex cases.

In this paper, we aim to provide an optimal passenger matching solution by recommending ridesharing groups of passengers from GPS trajectories. Existing algorithms for rider grouping usually rely on matching pre-selected origin-destination coordinates. Unfortunately, the semantics in the spatial layout (e.g., social interactions and properties of the locations) are ignored, leading to inaccuracies in discovering the ridesharing groups. Meanwhile, the destinations manually entered by users impact the accuracy of matching, as these addresses are usually not available in a road network or are not optimal for passenger pickup. This is particularly true when a passenger travels in a less familiar place. Given a set of passengers and the distribution of their destination, our approach is to compute the ridesharing matching between passengers. The raw GPS trajectories can be characterized by a combination of time constraints, traffic environments, and social activities. We first developed a PrefixSpan-prediction using a partial matching (P-PPM) destination-prediction algorithm to mine the frequent movement patterns from the trajectory data and determine the confidence of the movement rules. Our method uses the total travel time as the matching objective. Our approach is superior to the baseline methods in terms of accuracy (increased from 46% to 80%). We have also achieved significant improvements on other metrics, such as users’ saved travel distance. We demonstrated that using our proposed method, a group of passengers could save over 19% of total travel miles, which shows that the ridesharing scheme could be effective.

The problem of optimizing the operation of Bus Rapid Transit (BRT) lines that share a common corridor is analyzed. The goal is to minimize passengers travel time, including waiting at stations. In the common corridor, stations are shared by several bus lines which present riding alternatives for passengers with origins and destinations within the corridor. Since buses from different lines interact in the corridor, optimal multi-line headways that consider headways across all lines can alter single-line headways so as to maximize user benefit. We develop the system model in a mathematical programming fashion, embedded with the control strategy as bus holding decision variables. The method, named Multi-line Integrated Holding Control Strategy (M-IHCS), implements the required bus headway regulation for multiple lines in an integrated and simultaneous manner. Numerical tests of the optimization approach developed for the system model show the distinctive features of the solutions, indicating the superiority of multi-line headway control over its single-line counterpart.

The study proposes a cooperative lane changing strategy to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). The cooperative strategy was implemented by the coordination of behaviors between the diverging vehicle and its cooperative vehicle on the target lane. The Minimizing Overall Braking Induced by Lane Changes Model (MOBIL) and Intelligent Driver Model (IDM) were modified to develop a simulation platform for a CAV environment. The optimal cooperative lane changing zones were firstly calculated by a heuristic algorithm, and then were applied in the simulation platform to implement the cooperative strategy. Various metrics were considered to evaluate the proposed strategy, including: total travel time, surrogate safety measures and traffic waves in the system. The experimental results showed that the length of the optimal cooperative zones obtained in our strategy were smaller than the fixed zone required in modified MOBIL strategy. Moreover, the results indicated that the cooperative strategy with the optimal zones, could improve traffic operation, traffic safety and traffic oscillation as compared to the modified MOBIL strategy with the fixed zone. The cooperative strategy can be potentially implemented nearby highway off-ramps by vehicle-based control, with the applications of the aforementioned cooperative zones.

The automated merging control is one of the connected and automated vehicle applications that is expected to improve the operation and safety of freeway merging areas. This study introduces a dynamic adaptive algorithm for automated merging control based on a cost function of travel time for both on-ramp and mainline vehicles. The developed algorithm is designed to guide on-ramp vehicles to merge efficiently, without frequent slowdown or wait for merging gaps at the end of the ramp and with minimal disruption to the mainline traffic. The algorithm was evaluated under different mainline traffic demands, ranging from light to heavy conditions, using a simulation model for a one-mile freeway segment with two lanes and a single-lane on-ramp. Platooning is implemented in the simulation based on Cooperative Adaptive Cruise Control. The performance of the algorithm is compared to a base case where mainline vehicles cooperatively decelerate to help on-ramp vehicles merge. The results show that the proposed algorithm reduced ramp delays in the range of 38% to 91% for different traffic conditions, without disrupting mainline operation. Additionally, travel time reliability index improved in the range of 18% to 48% under different traffic conditions. According to the simulation results, the proposed automated merging algorithm is henceforth considered reliable, with its promising performance.

The efficient use of the road network for freight transport has a big impact on travel times, pollution, and fuel consumption, as well as on the mobility of passenger vehicles. In today’s road network, truck drivers make uncoordinated selfish routing decisions, which may easily congest an initially uncongested route as many truck drivers make the same selfish decision by choosing the same route in an effort to minimize their travel time without accounting for the fact that others do the same, given the same available traffic information. In this paper, we propose a coordinated system for truck drivers, using monetary incentives and fees, to balance the traffic load and improve the overall traffic conditions and time delays experienced by both truck and passenger vehicle drivers. The basic characteristics of the mechanisms presented are that they are budget balanced, do not penalize the truck drivers compared to the user equilibrium, and they assume voluntary participation. Two models of voluntary participation are considered: weak and strong voluntary participation. In the first, each one of the drivers prefers all the drivers (including self) to participate in the mechanism than not. In the second model, each one of the truck drivers prefers to participate in the system, provided that all the others do. For each model of voluntary participation, an incentive mechanism is designed. A special emphasis is given to the fairness of the proposed mechanisms. The numerical examples are used to demonstrate the results and the efficiency of the solution techniques.

The literature on green mobility and eco-driving in urban areas has burgeoned in recent years, with special attention to using infrastructure to vehicle (I2V) communications to obtain optimal speed trajectory which minimize the economic and environmental costs. This article shares the concept with these studies but turns the spotlight on cyclists. It examines the problem of finding optimal speed trajectory for a cyclist in signalised urban areas. Unlike the available studies on motorised vehicles which predominantly designed for pre-defined, fixed traffic lights timing, this article uses an algorithm based on stochastic dynamic programming to explicitly address uncertainty in traffic light timing. Moreover, through a comprehensive set of simulation experiments, the article examines the impact of the speed advice's starting point as well as the cyclist's willingness for changing his/her speed on enhancing the performance. The proposed approach targets various performance metrics such as minimising the total travel time, energy consumption, or the probability of stopping at a red light. Hence, the resulting speed advice can be tailored according to the personal preferences of each cyclist. In a simulation case study, the results of the proposed approach is also compared with an existing approach in the literature.

The possibility of having driverless cars on the streets seems to be more real than ever. In this paper, we focus on developing exact methods that can determine the effects of privately owned automated vehicles (AVs) and how switching to those vehicles is going to change mobility in urban environments. The considered problem determines the routes of family owned AVs that minimize the transportation costs of that family while considering the possibility of using public transport as an alternative for some trips. We introduce a novel exact linear formulation for this problem which includes a linearized traffic congestion model and which is able to solve the user and system optimum variant of the problem to optimality. The introduced formulation can easily be adapted to consider the current situation with conventional vehicles and a situation where not only the travel time costs of the driver but also costs of the other passengers are taken into account. The main advantage of our novel formulation is that the optimal results can be obtained to explore potential changes of flows with vehicle automation in small networks. We investigated the behavior of the system, given the described scenarios, by applying our formulation to a case study.

The sharp increase in e-commerce over the last few years has led to an increase in the volume of trucks both in ports and in commercial areas. Truck traffic has a negative impact on traffic flow in general due to the size of trucks and their slower dynamics. The continuously increasing use of navigation apps has led drivers to make their routing decisions in an independent manner in an effort to minimize their own individual travel time, with possible significant deviation from a socially optimum solution. In this paper, we consider the use of coordinated routing in order to achieve load balancing. Users send their OD matrices as well as their preferred departure time to the coordinator who gives them routing instructions based on a socially optimum cost. This design enables us to derive sufficient conditions under which we prove the existence of mechanisms which are truthful in equilibrium, budget balanced on average and create individual incentives for voluntary participation of the truck drivers. Subsequently, we design our mechanism in a way that only uses a minimal set of sufficient conditions in order to guarantee the existence of a solution and maximize its efficiency. Finally, the extensive simulation results of our approach in the Braess and the Sioux Falls networks demonstrate that the proposed mechanism can approach the system optimum solution.

Vehicle scheduling is an important component of the transit operations planning process because it is directly related to the number of vehicles required for covering all trips. However, in practice, vehicles are not scheduled by a very efficient approach, related to the fact that all vehicles use one type of operation strategy—full operation strategy—for meeting unbalanced demand patterns. This paper proposes a methodology to reduce the required number of vehicles by adjusting a few trips to operate by other strategies, including limited stop, short turn, deadheading, and mixed strategies. This proposed methodology is based on a graphical human–machine interactive technique, called deficit function (DF). The DF allows schedulers to select a variable trip schedule for reducing the number of vehicles using optimal determination of the best applied strategies. The process of adjusting variable trips to accommodate different strategies may result in passenger travel time changes. Two optimization models are developed to minimize these changes and determine a set of stops served by the variable trip schedule. This proposed methodology has been applied to a detailed example and a case study in Paris, France. The results show that the adjustment of a few trips to use certain strategies can reduce the number of buses required by three.

This paper introduces a simple but practical approach that uses both loop detector data and probe travel times for computing the vehicle hours traveled (VHT), average delay, and level of service (LOS) for signalized intersections. The goal is to improve upon the state-of-the-practice method outlined in the highway capacity manual (HCM) by incorporating additional travel time measurements from probe vehicles or vehicle re-identification systems. The proposed methodology is designed to work under a variety of traffic conditions, including states of congestion in which the HCM methodology is not reliable. Our analysis is then tested using simulation of an arterial site in Arcadia, CA, USA. The results suggest that the proposed methodology performs better at the approach level than at the lane group level. Population size and probe penetration rate are two key parameters in the estimation. Either a large population size or a high penetration rate is required in order to produce reliable estimates of VHT, delay, and LOS. Results also show that the proposed methodology only requires 7% of the penetration rate to outperform the HCM methodology.

Variable speed limit (VSL) control of highway traffic is expected to improve traffic mobility, safety, and environment, especially during incidents. However, most existing VSL controllers show significant benefits in macroscopic analysis but little improvement in microscopic simulations in terms of traffic mobility. We demonstrate that the lack of improvement for travel time in many incident cases is due to lane changes that are taking place close to the bottleneck leading to severe capacity drop, which is not adequately captured by most macroscopic models. In this paper, we develop a combined lane change and VSL control scheme, which generates consistent improvements both with macroscopic and microscopic models. The lane change controller generates lane change recommendations upstream the incident or bottleneck in order to reduce the effect of the capacity drop. The VSL controller is developed using a feedback linearization approach based on the cell transmission macroscopic model and is shown analytically to guarantee exponential convergence to the optimum equilibrium point. Microscopic Monte Carlo simulations of traffic on the I-710 freeway were used to demonstrate that this combined control strategy is able to generate consistent improvements with respect to travel time, safety, and environmental impact under different traffic conditions and incident scenarios. © 2017 IEEE. 18 7 1812 1823

Recognition of necessities of connected and automated vehicles (CAVs) is gaining momentum. CAVs can improve both transportation network efficiency and safety through control algorithms that can harmonically use all existing information to coordinate the vehicles. This paper addresses the problem of optimally coordinating CAVs at merging roadways to achieve smooth traffic flow without stop-and-go driving. We present an optimization framework and an analytical closed-form solution that allows online coordination of vehicles at merging zones. The effectiveness of the efficiency of the proposed solution is validated through a simulation, and it is shown that coordination of vehicles can significantly reduce both fuel consumption and travel time.

Travel time and demand disturbances lead to unreliable bus operations and missed passenger transfers. This study formulates the multi-line synchronization problem as a robust min(i)max problem that considers the fluctuations of the travel and dwell times of bus trips. Given the infeasibility of the multi-line synchronization problem in extreme cases of travel/dwell time disturbances, we introduce a flexible problem formulation that incorporates the constraint violations into the objective function. To produce a robust schedule, the dispatching times of trips are our design variables and the travel and dwell time fluctuations are the environmental variables which have an adversarial role in our minimax problem. We validate our approach in the bus network of The Hague using 1 month of actual vehicle location and passenger counting data. There, we demonstrate the potential improvement in terms of service regularity and increased synchronizations in common case and extreme case conditions.

This paper advocates a new paradigm of transportation systems for future smart cities, namely, public vehicles (PVs), that provides dynamic ridesharing trips at requests. Passengers will enjoy more convenient and flexible transportation services with much less expense. In the PV system, both the number of vehicles and required parking spaces will be significantly reduced. There will be less traffic congestion, less energy consumption, and less pollution. In this paper, the concept, method, and algorithm for the PV system are described. The key issue of effectively implementing the PV system is to design efficient planning and scheduling algorithms. The PV-path problem is formulated, which is NP-complete. Then, a practical approach is proposed, which can serve people anywhere and anytime. The simulation results show that, to achieve the same performance (e.g., total time, waiting time, and travel time), the number of vehicles in the PV system can be reduced by around 90% and 57% compared with the conventional vehicle system and Uber Pool, respectively, and the total traveling distance can be reduced by 34% and 14%.

Recent advances in autonomous vehicles and vehicular communications are envisioned to enable novel approaches to managing and controlling traffic intersections. In particular, with intersection controller units (ICUs), passing vehicles can be instructed to cross the intersection safely without traffic signals. Previous efforts on autonomous intersection control mainly focused on guaranteeing the safe passage of vehicles and improving intersection throughput, without considering the quality of the travel experience from the passengers' perspective. In this paper, we aim to design an enhanced autonomous intersection control mechanism, which not only ensures vehicle safety and enhances traffic efficiency but also cares about the travel experience of passengers. In particular, we design the metric of smoothness to quantitatively capture the quality of experience. In addition, we consider the travel time of individual vehicles when passing the intersection in scheduling to avoid a long delay of some vehicles, which not only helps with improving intersection throughput but also enhances the system's fairness. With the above considerations, we formulate the intersection control model and transform it into a convex optimization problem. On this basis, we propose a new algorithm to achieve an optimal solution with low overhead. Finally, we build the simulation model and implement the algorithm for performance evaluation. Comprehensive simulation results demonstrate the superiority of the proposed algorithm.

Despite the great demand on and attempts at package express shipping services, online retailers have not yet had a practical solution to make such services profitable. In this paper, we propose an economical approach to express package delivery, i.e., exploiting relays of taxis with passengers to help transport package collectively, without degrading the quality of passenger services. Specifically, we propose a two-phase framework called crowddeliver for the package delivery path planning. In the first phase, we mine the historical taxi trajectory data offline to identify the shortest package delivery paths with estimated travel time given any Origin–Destination pairs. Using the paths and travel time as the reference, in the second phase we develop an online adaptive taxi scheduling algorithm to find the near-optimal delivery paths iteratively upon real-time requests and direct the package routing accordingly. Finally, we evaluate the two-phase framework using the real-world data sets, which consist of a point of interest, a road network, and the large-scale trajectory data, respectively, that are generated by 7614 taxis in a month in the city of Hangzhou, China. Results show that over 85% of packages can be delivered within 8 hours, with around 4.2 relays of taxis on average.

© 2000-2011 IEEE. Transport researchers and practitioners have long been seeking capable solutions to deal with the traffic oscillations caused by freeway merging. Although existing approaches based on ramp metering have improved the overall efficiency of on-ramps, their performance is still far below the theoretical capacity. The recently proposed detecting technology of autonomous vehicles (AVs) provides an alternative for maximizing the merging efficiency by developing and using appropriate controllers for AVs. In this paper, we develop a cooperative intelligent driver model in order to examine the system performance under different proportions of AVs. The results show that, with a proper vehicle-to-vehicle controlling mechanism, an increasing percentage of AVs will reduce the total travel time and smooth traffic oscillations.

Safety, capacity, and timely schedules are some of the most crucial objectives in railway operations. Positive train control (PTC) is a concept whose goal is to improve the safety and efficiency of railway operations by using advanced information technologies. Information technologies such as active communications enable the use of a dynamic headway policy, which can increase the track capacity and improve dispatching efficiency in addition to improving safety. In this paper, we propose a dynamic headway system for PTC based on active communications, which we integrate with a dynamic dispatching model in order to improve track capacity and safety in railway operations. We use a simulation model of a rail network in southern California to demonstrate the effectiveness of our proposed approach. The simulation results of different scenarios show reductions in train delays of at least 55% and reductions in travel time of at least 35% when using the dynamic headway versus using a fixed headway.

The papers in this special section are devoted to the topic of Big Data for intelligent vehicle systems. issue. The collection of papers included can be categorized in several ways. In terms of applications, the special section covers transit network design, taxi and bus operation, traffic flow and travel time prediction, dilemma zone management, and geometric design of roads. In terms of data sources, it covers both conventional sources of data such as bus, taxi, and individual vehicle trajectories, and also novel sources of data such as those based on the connected vehicle technology, roadside communication equipment, or onboard devices. In terms of techniques or methodologies developed for processing big data, this collection includes papers covering the data imputation technique based on the k-nearest neighbor method, data management infrastructure, the machine learning approach, and the graph-theory-based method.

This paper proposes a vehicle reidentification (VRI) system with self-adaptive time windows to estimate the mean travel time for each time period on the freeway under traffic demand and supply uncertainty. To capture the traffic dynamics in real-time application, interperiod adjusting based on the exponential smoothing technique is introduced to define an appropriate time-window constraint for the VRI system. In addition, an intraperiod adjusting technique is also employed to handle the nonpredictable traffic congestion. To further reduce the negative effect caused by the mismatches, a postprocessing technique, including thresholding and stratified sampling, is performed on the travel time data derived from the VRI system. Several representative tests are carried out to evaluate the performance of the proposed VRI against potential changes in traffic conditions, e.g., recurrent traffic congestion, freeway bottlenecks, and traffic incidents. The results show that this method can perform well under traffic demand and supply uncertainty.

We present and evaluate our distributed and self-adaptive vehicle routing guidance approach, termed BeeJamA, which provides drivers safely with routing directions well before each intersection. Our approach is based on a multiagent system, which is inspired by the honey bee foraging behavior. It relies on a distributed vehicle-to-infrastructure architecture. On the basis of microscopic traffic simulations under varying penetration rates, we show that BeeJamA outperforms dynamic shortest path algorithms with respect to average (global) travel times and regarding congestion avoidance.

Travel time prediction in Advanced Traveler Information Systems is an important issue, because travel time is a major factor in motorists' decisions to avoid congestion and incidents. A simulation-assignment-based travel time prediction model for traffic corridors is constructed in this paper. Based on the concept of simulation-assignment models, two algorithms-the flowand the vehicle-based models-are proposed for travel time prediction. One of the critical issues in simulation-assignment models is how reliable time-dependent origin-destination (O-D) trip tables are generated. A dynamic O-D estimation and prediction procedure is developed to generate time-dependent O-D demand data for simulating vehicle movements using DynaTAIWAN: a simulation-assignment model. The empirical travel time data that were collected from electronic toll stations are used to validate the travel times that were predicted by the proposed models. The mean absolute percentage errors (RMSPEs) and root-mean-square percentage errors are less than 20% and 26% for the vehicle-based model and less than 10% and 12% for the flow-based model, respectively. The results show that the proposed algorithms predict reasonable travel times for traffic corridors.

The objective of this paper is to collect and analyze data that can be used to model mode- and different route-choice paradigms using same subjects and same experiment. In this paper, the authors estimated five models to address drivers' diversion, compliance, and route choice. In addition, the effect of advanced traveler information systems (ATIS) on the mode choice is also considered. A travel simulator was used as a dynamic data collection tool. The simulator uses a realistic network, two modes of travel, real historical volumes, and different weather conditions. It provides five different levels of traffic information/advice, one at a time, and collects dynamic mode choices and pretrip (long-term) and en-route (short-term) route choices. The binomial and multinomial generalized extreme equations (BGEE and MGEE) were used to account for the correlation between repeated choices made by the same subject. In addition, MGEE accounts for the correlation between alternatives in multidimensional route-choice models. The modeling results showed that travel time and familiarity with the device that provides the information had significant effects on the first four models. It is shown that developing in-vehicle information devices may lead to a less transit usage in some cases; this indicates a potential drawback of this technology. Expressway users are shown as the most travel-time savers who would divert if they are guided to a less-travel-time alternative. The number of traffic signals on the normal and advised routes affects the diversion from the normal route and compliance with the pretrip advised route. This paper underlines the importance of modeling correlation, if it exists, in mode/route-choice data

Vehicle reidentification is the process of matching vehicles from one point on the roadway (one field of view) to the next. By performing vehicle reidentification, important traffic parameters including travel time, travel time variability, section density, and partial dynamic origin/destination demands can be obtained. Field traffic data were collected in Alton Parkway in Southern California for training and testing of the multidetector vehicle reidentification algorithm. These data consisted of inductive loop signatures of vehicles that traversed two detector stations spanning a section of an arterial and the corresponding video of these signatures. Even though the video collected was not optimized for pattern-recognition purposes, an investigation into the feasibility of fusing inductive vehicle signatures with video for anonymous vehicle reidentification was conducted. The resulting reidentification rate of over 90% shows that this approach merits further investigation. The results also show that the use of detector fusion provides system redundancy and yields slightly better results than the use of a single detector.