Connected vehicle enabled hierarchical anomaly behavior management system for city-level networks

Vehicle incidents on roads result in lane closure and severe traffic congestion, and the frequent mandatory lane changes of the upstream vehicles generate capacity drops ahead of the incidents, which further increase road congestion. With the development of connected vehicles, vehicle incidents can be detected by individual vehicles, and immediate driving assistance can be provided to help them pass the incidents efficiently. This paper proposes a distributed lane-changing assistant (DLCA) system with connected vehicles to advise individual vehicles with the optimal lanes to pass incidents with smaller delays. The system introduces connected vehicles to detect the location and the lane closure information of an incident and broadcast the information to the upstream connected vehicles. To determine the optimal lane for each connected vehicle, a speed index is defined for each lane based on the incident information and the downstream connected vehicle dynamics. The DLCA system is evaluated with a microscopic traffic simulator, INTEGRATION, to illustrate its benefits in improving the performance of individual vehicles and mitigating road congestion. A sensitivity analysis of market penetration rates and demand levels of connected vehicles is also conducted in this paper. The results indicate that the DLCA system can reduce the delay by about 22.1% for the connected vehicles, and it has higher benefits on improving the performance of the entire road at higher market penetration rates. In addition, there exists an optimal demand level to maximize the benefits of the system.

A dynamic spatial–temporal deep learning framework for traffic speed prediction on large-scale road networks

Various collision avoidance systems (CASs) have been developed and employed in human-operated vehicles as well as more recently in autonomous vehicles. Most of the existing CASs perform an override function to actuate automatic emergency braking in a critical situation based on the current traffic information obtained from in-vehicle sensors or short-range vehicular communications. These CASs focus on the critical situation in the vicinity of the subject vehicle, which means they may have negative influences on the subject vehicle and its following vehicles, particularly when the leader vehicle of a platoon with short headway applies harsh braking to mitigate an impending collision risk. This study proposes a risk predictive CAS (RPCAS) which executes predictive deceleration with mild braking in advance to prevent a potential rear-end collision by predicting the collision risk arising from a downstream site. To evaluate the performance of the RPCAS, the proposed system is compared with several existing CASs in various car-following cases based on a microscopic traffic simulation. The simulation results show that the RPCAS can effectively reduce the rear-end collision risk with less harsh braking compared with the existing CASs. Furthermore, the RPCAS enables vehicles arriving from upstream to anticipate a potential crash, which provides them with sufficient time to reduce their current speeds proactively. The research findings suggest that the proposed system can attenuate the negative impacts of the previous CASs in relation to traffic and vehicular safety.

Beijing, a world famous historic metropolitan city, is facing serious traffic congestion in its central business district due to the ever increasing traffic demands induced by high speed economic development. To improve the local traffic situation, how to plan the road network in this area and protect the cultural relics simultaneously has become a difficult problem that concerns the government decision-makers and urban planners. The purpose of this paper is to present a case study of microscopic traffic simulation in the road network planning of Chaoyangmen-Fuchengment Street in Beijing city. A high performance microscopic traffic simulation tool, PARAMICS (PARAllel MICroscopic Simulator), is selected to model, visualize, evaluate and compare two types of schemes, i.e. public transportation dedicated lane schemes and one-way traffic schemes. This study indicates that microscopic traffic simulation is able to provide quantitative and effective decision support for the road network planning.

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. The calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

Traffic congestion in urban areas is posing many challenges, and a traffic flow model that accurately predicts traffic conditions can be useful in responding to them. With the limitation of infrastructure and the difficulty for real world experiment, traffic simulation is a good tool for the validation and future potential applications of the traffic flow model. This research presents a stochastic traffic flow model which uses a stochastic partial differential equation to describe the evolution of traffic flow on the highway. The stochastic model is calibrated and validated by real traffic data on a highway and is proved to have better predictive power than the deterministic model. Also, a microscopic traffic simulation is built and calibrated by the same highway data, and the validation result shows that the simulation is capable of representing the real traffic. By using traffic simulation to further explore the capability of the stochastic model, the validation demonstrated that the prediction works well at most locations, and the interpolation error may be improved by considering the influence of ramps and the change in the number of lanes.

This study analyzed traffic congestion scenarios in underground roads diverging and merging sections and underground sections that run parallel to ground roads using a microscopic traffic simulation model. The appropriate ratio of the ramp inflow or outflow of traffic volume to the entry traffic volume at the starting section of the underground road network was analyzed. Furthermore, the effect of traffic operation such as adequate throughput, average traffic speed, occupancy, and volume versus capacity (V/C) ratios before the occurrence of traffic congestion in all three road networks was properly produced. To maintain a minimum level of service of all underground sections at the D–E level, the underground inflow traffic volume should be equal to approximately 70% of the 4000 pcph inflow traffic volume from an open road to the underground. The results of this study are a useful reference for establishing traffic control application standards to minimize underground traffic congestion. They also provide useful insights for underground road design.

Hybrid deep learning models for traffic prediction in large-scale road networks

Microscopic traffic simulation is a well-accepted simulation approach for simulation problems where the effects of individual driver behavior and/or vehicle interactions need to be taken into account at a fairly detailed level. Such problems include the optimization of traffic light controls patterns or the design of lane layouts at intersections. Such simulation models typically require very detailed and accurate models of the underlying road networks. The manual creation of such networks constitutes a high effort, limiting the simulated area in practical applications to the absolutely necessary. With the increased availability of satellite based geographical data we investigate, if and how such data can be automatically transformed into route networks with adequate level of detail for microscopic traffic simulation models. We further outline the design of data structures for an extensible simulation framework for microscopic traffic simulation which is capable of including different types of publically available data sources.

This paper examines the application of Latin Hypercube Sampling (LHS) and Antithetic Variables (AVs) to reduce the variance of estimated performance measures from microscopic traffic simulators. LHS and AV allow for a more representative coverage of input probability distributions through stratification, reducing the standard error of simulation outputs. Two methods of implementation are examined, one where stratification is applied to headways and routing decisions of individual vehicles and another where vehicle counts and entry times are more evenly sampled. The proposed methods have wider applicability in general queuing systems. LHS is found to outperform AV, and reductions of up to 71% in the standard error of estimates of traffic network performance relative to independent sampling are obtained. LHS allows for a reduction in the execution time of computationally expensive microscopic traffic simulators as fewer simulations are required to achieve a fixed level of precision with reductions of up to 84% in computing time noted on the test cases considered. The benefits of LHS are amplified for more congested networks and as the required level of precision increases.

Common car-following models for microscopic traffic simulation assume a time advancement using fixed-sized time steps. However, a purely time-driven execution is inefficient when the states of some agents are independent of other agents and thus predictable far into the simulated future. We propose a method to accelerate microscopic traffic simulations based on identifying independence among agent state updates. Instead of iteratively updating an agent’s state throughout a sequence of time steps, a computationally inexpensive “fast-forward” function advances the agent’s state to the time of its earliest possible interaction with other agents. We present an algorithm to determine independence intervals in microscopic traffic simulations and derive fast-forward functions for several well-known traffic models. In contrast to existing approaches based on reducing the level of detail, our approach retains the microscopic nature of the simulation. An evaluation is performed for a synthetic scenario and on the road network of Singapore. At low traffic densities, maximum speedup factors of about 2.6 and 1.6 are achieved, while at the highest considered densities, only few opportunities for fast-forwarding exist. We show that the deviation from purely time-driven execution is reduced to a minimum when choosing an adequate numerical integration scheme to execute the time-driven updates. Verification results show that the overall deviation in vehicle travel times is marginal.

Ride sharing has been widely studied in academia and applied in mobility-on-demand (MoD) systems as a means of reducing the number of cars, congestion, and pollution by sharing empty seats. Solving this problem is challenging on large-scale road networks for the following two reasons: distance calculation on large-scale road networks is time consuming; and multi-request allocation and multi-point planning have been proved to be NP-hard problems. In this paper, we propose a clustering-based request matching and route planning algorithm Roo that considers spatial-temporal distances between ride requests on road networks. The Roo algorithm is evaluated with real-world taxi trajectory data and road networks from New York City and Beijing. The results show that Roo can save up to 50% of mileage by 1000 vehicles serving around 7000 trip requests in New York City between 7:40 am to 8:00 am with average waiting time of 4 minutes.

Microscopic simulation models have been widely used as tools to investigate the operation of traffic systems and different intelligent transportation systems applications. The fidelity of microscopic simulation tools depends on the driving behavior models that they implement. However, current models commonly do not consider human-related factors, such as distraction. The potential for distraction while driving has increased rapidly with the availability of smartphones and other connected and infotainment devices. Thus, an understanding of the impact of distraction on driving behavior is essential to improve the realism of microscopic traffic tools and support safety and other applications that are sensitive to it. This study focuses on car-following behavior in the context of distracting activities. The parameters of the well-known GM and intelligent driver models are estimated under various distraction scenarios using data collected with an experiment conducted in a driving simulator. The estimation results show that drivers are less sensitive to their leaders while talking on the phone and especially while texting. The estimated models are implemented in a microscopic traffic simulation model. The average speed, coefficient of variation of speed, acceleration noise and acceleration and deceleration time fractions were used as measures of performance indicating traffic flow and safety implications. The simulation results show deterioration of traffic flow with texting and to some extent talking on the phone: average speeds are lower and the coefficient of variation of speeds are higher. Further experimentation with varying fractions of texting drivers showed similar trends.

Road network traffic data has been widely studied by researchers and practitioners in different areas such as urban planning, traffic prediction and spatial-temporal databases. The existing urban traffic simulators suffer from two critical issues (1) scalability: most of them only offer single-machine solutions which are not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, and car following. In the paper, we propose GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. A full-fledged prototype of GeoSparkSim is implemented in Apache Spark. In this demonstration, we will show the attendees how to issue GeoSparkSim simulation tasks via the user interface, visualize simulated vehicle movements, and monitor the backend Spark cluster status.

A Microscopic Traffic Simulator for evaluation of dynamic traffic management systems