Vehicle Infrastructure Integration Research Articles

Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost

China’s widely adopted expressway ETC system provides a feasible foundation for realizing co-operative vehicle–infrastructure integration, and the accuracy of ETC data, which forms the basis of this scheme, will directly affect the safety of driving. Therefore, this study focuses on the abnormal data in an expressway ETC system. This study combines road network topology data and capture data to mine the abnormal patterns of ETC data, and it designs an abnormal identification model for expressway transaction data based on TL-XGBoost. This model categorizes expressway ETC abnormal data into four distinct classes: missing detections, opposite lane detection, duplicated detection and reverse trajectory detection. ETC transaction data from a southeastern Chinese province were used for experimentation. The results validate the model’s effectiveness, achieving an accuracy of 98.14%, a precision of 97.59%, a recall of 95.44%, and an F1-score of 96.49%. Furthermore, this study conducts an analysis and offers insights into the potential causes of anomalies in expressway ETC data.

A Dynamic Predictive Traffic Signal Control Framework in a Cross-Sectional Vehicle Infrastructure Integration Environment

With the development of modern wireless communication technology, especially the vehicle infrastructure integration (VII) technology, vehicles’ information such as identification, location, and speed can be readily obtained at upstream cross-section. This information can be used to support traffic signal timing optimization in real time. A dynamic predictive traffic signal control framework for isolated intersections is proposed in a cross-sectional VII environment, which has the ability to predict vehicle arrivals and use this to optimize traffic signals. The proposed dynamic predictive control framework includes a dynamic platoon dispersion model (DPDM) which uses the vehicles’ speed data from the cross-sectional VII environment, as opposed to traditional vehicle passing/existing data, to predict the arriving flow distribution at the downstream stop-line. Then, a dynamic programming algorithm based on the exhaustive optimization of phases (EOP) is proposed working in rolling optimization (RO) scheme with a 2s time horizon. The signal timings are continuously optimized by regarding the minimization of intersection delay as the optimization objective, and setting the green time duration of each phase as a constraint. In the end, the proposed dynamic predictive control framework is tested in a simulated cross-sectional VII environment and a case study carried out based on a real road network. The results show that the proposed framework can reduce the average delay and queue length by up to 33% and 35%, respectively, compared with the traditional full-actuated control.

Active signal priority control method for bus rapid transit based on Vehicle Infrastructure Integration

The implementation of signal priority control to reduce delays of BRT vehicles at signalized intersections is of practical and theoretical significance. In this paper, we propose an active signal priority control method for BRT vehicles that run on median-road exclusive BRT lanes at single intersections based on Vehicle Infrastructure Integration system. This method aims at maximizing average passenger benefit of BRT and other road users, and provides 8 signal priority control scenarios respectively for 8 BRT arrival modes that are based on estimating BRT vehicle travel time and locating arriving time window in a cycle. The delay, energy efficiency and passengers’ comfort of BRT vehicles, and community vehicles’ efficiency are also being considered. Finally, a model simulation was conducted by VISSIM modeling in a representative signalized intersection with BRT in Jinan City, China. The results indicate that the proposed method reduces average passenger delay by 13.43–25.27% and improves travel speed of BRT vehicles by 7.10–7.55% comparing to existing signal control scenarios. The proposed method is highly promising and can be applied to improve efficiency and safety of BRT at signalized intersections.

Using Intelligent Vehicle Infrastructure Integration for Reducing Congestion in Smart City

This paper proposes a novel cognitive cellular automata (CA) approach to traffic management that can adapt to immediate requirements, be applied for use in cross-area car societies, enhance system performance, and decrease traffic congestion problems. We propose a mechanism that operates in a cognitive radio mode to increase the channel-reuse rate and decrease the allocation of redundant channels. This approach provides the advantage of a heterogeneous communication interface based on cognitive mechanisms that recognize different transmission modulation modes. The receiver gets messages through different transmission modulation modes. In this work, we postulate vehicles connecting to traffic congestion computing centers by vehicle-to-roadside communications within a car society. Roadside units serve each road segment, and we suppose that every car has a navigation device. We propose an innovative congestion-reduction mechanism that provides directions to a vehicle's navigation device after the driver sets the origin location and the destination. This mechanism calculates the congestion status of the upcoming road segment. By tracking the status of road segments from a point of origin to a destination, our proposed mechanism can handle cross-area car societies. The current study evaluates this approach's performance by conducting computer simulations. Simulation results reveal the strengths of the proposed CA mechanism in terms of increased lifetime and increased congestion-avoidance for urban vehicular networks.

Intersection Management via Vehicle Connectivity: The Intersection Cooperative Adaptive Cruise Control System Concept

Since the introduction of the vehicle infrastructure integration (VII) and connected vehicle (CV) initiatives in the United States, numerous in-vehicle technologies based on wireless communications are currently being deployed. One of these technologies is cooperative adaptive cruise control (CACC) systems, which provide better connectivity, safety, and mobility by allowing vehicles to travel in denser platoons through vehicle-to-vehicle (V2V) communication. Accordingly, the research presented in this article develops a simulation/optimization tool that optimizes the movement of CACC-equipped vehicles as a replacement for traditional intersection control. This system, which is named iCACC, assumes that the intersection controller receives vehicle requests to travel through an intersection and advises each vehicle on the optimum course of action ensuring no crashes occur while at the same time minimizing the intersection delay. Four intersection control scenarios are compared, namely: a traffic signal, an all-way stop control (AWSC), a roundabout, and the iCACC controller. The results show that the proposed iCACC system significantly reduces the average intersection delay and fuel consumption level by 90 and 45%, respectively. Additionally, the article investigates the impact of vehicle dynamics, weather conditions, and level of market penetration of equipped vehicles on the future of automated vehicle control.

Variable Speed Limit Design to Relieve Traffic Congestion based on Cooperative Vehicle Infrastructure System

To uniform traffic flow, and to improve traffic mobility and safety, variable speed limit (VSL) is usually implemented on freeways. With predictive models, traffic states evolutions can be predicted, so VSL values can be optimized within model predictive control (MPC) frameworks to keep traffic flow at a high efficiency and suppress propagation of shockwaves, especially during congestion periods. METANET model is a widely used predictive tool, as well as its many extensions. In this paper, a new extension of METANET is proposed based on cooperative vehicle infrastructure system (CVIS), also known as vehicle-infrastructure integration (VII) system. By introducing vehicle models, vehicles’ speed and position on each link are recorded, and prediction is changed to a mesoscopic scale, which describes the traffic entities at a high level of detail, but describes their behaviour and interactions at a lower level of detail (Van Woensel et al., 2007). Compared with previous studies that use macroscopic variables for prediction, the new model suits the situations in CVIS, where communication between vehicles and infrastructures will be adopted to replace variable message signs (VMS). Simulation results have shown the capacity of the proposed model in predicting state evolutions. Additionally, VSL's ability to improve traffic efficiency and prevent congestion propagation is proved.

Hybrid powertrain optimization with trajectory prediction based on inter-vehicle-communication and vehicle-infrastructure-integration

Recent advances in Inter-Vehicle Communications (IVC) and Vehicle-Infrastructure Integration (VII) paved ways to real-time information sharing among vehicles, which are beneficial for vehicle energy management strategies (EMS). This is especially valuable for power-split hybrid electrical vehicles (HEV) in order to determine the optimal power-split between two different power sources at any particular time. Certainly, researches in this area have been done, but tradeoffs between optimality, driving-cycle sensitivity, speed of calculation and charge-sustaining (CS) conditions have not been cohesively addressed before. In light of this, a combined approach of a time-efficient powertrain optimization strategy, utilizing trajectory prediction based on IVC and VII is proposed. First, Gipps’ car following model for traffic prediction is used to predict the interactions between vehicles, combined with the cell-transmission-model (CTM) for the leading vehicle trajectory prediction. Secondly, a computationally efficient charge-sustaining (CS) HEV powertrain optimization strategy is analytically derived and simulated, based on the Pontryagin’s Minimum Principle and a CS-condition constraint. A 3D lookup-map, generated offline to interpolate the optimizing parameters based on the predicted speed, is also utilized to speed up the calculations. Simulations are conducted for 6-mile and 15-mile cases with different prediction update timings to test the performance of the proposed strategy against a Rule-Based (RB) control strategy. Results for accurate-prediction cases show 9.6% average fuel economy improvements in miles-per-gallon (MPG) over RB for the 6-mile case and 7% improvements for the 15-mile case. Prediction-with-error cases show smaller average MPG’s improvements, with 1.6% to 4.3% improvements for the 6-mile case and 2.6% to 3.4% improvements for the 15-mile case.

Integrated Traffic and Communication Performance Evaluation of an Intelligent Vehicle Infrastructure Integration (VII) System for Online Travel-Time Prediction

This paper presents a framework for online highway travel-time prediction using traffic measurements that are likely to be available from vehicle infrastructure integration (VII) systems, in which vehicle and infrastructure devices communicate to improve mobility and safety. In the proposed intelligent VII system, two artificial intelligence (AI) paradigms, i.e., artificial neural networks (ANNs) and support vector regression (SVR), are used to determine future travel time based on such information as the current travel time and VII-enabled vehicles' flow and density. The development and performance evaluation of the VII-ANN and VII-SVR frameworks, in both the traffic and communications domains, were conducted using an integrated simulation platform for a highway network in Greenville, SC. In particular, the simulation platform allows for implementing traffic surveillance and management methods in the traffic simulator PARAMICS and for evaluating different communication protocols and network parameters in the communication network simulator, Network Simulator version 2 (ns-2). This paper's findings reveal that the designed communications system can support the travel-time prediction functionality. The findings also demonstrate that the travel-time prediction accuracy of the VII-AI framework was superior to a baseline instantaneous travel-time prediction algorithm, with the VII-SVR model slightly outperforming the VII-ANN model. Moreover, the VII-AI framework was shown to perform reasonably well during nonrecurrent congestion scenarios, which have traditionally challenged sensor-based highway travel-time prediction methods.

Designing Yellow Intervals for Rainy and Wet Roadway Conditions

The research presented in this paper quantifies and models the impact of wet pavement surface and rainy weather conditions on driver perception-reaction times (PRTs), deceleration levels, and traffic signal change interval durations. A total of 648 stop-run records were collected as part of the research effort for a 72 km/h (45 mi/h) approach speed where participant drivers encountered a yellow indication initiation at different distances from the intersection. The participant drivers were randomly selected in different age groups (under 40 years old, 40 to 59 years old, and 60 years of age or older) and genders (female and male). Using the gathered data, statistical models for driver PRT and deceleration levels were developed, considering roadway surface and environmental parameters, driver attributes (age and gender), roadway grade, approaching speed, and time and distance to the intersection at the onset of yellow. Inclement weather yellow timings were then developed and summarized in lookup tables as a function of different factors (driver age/gender, roadway grade, speed limit, precipitation level, and roadway surface condition) to provide practical guidelines for the design of yellow signal timings in wet and rainy weather conditions. The results indicate that wet roadway surface conditions require a 5 percent increase in the change interval and that rainy conditions require a 10 percent or more increase in the duration of the change interval. These recommended change durations can also be integrated within the Vehicle Infrastructure Integration (VII) initiative to provide customizable driver warnings prior to a transition to a red indication.

RSU LOCALIZATION MODEL AND SIMULATION OPTIMIZATION FOR VII NETWORK

While facing the needs for Vehicle Infrastructure Integration (VII) applications in traffic management, the paper deals with the problem of locating Road Side Units (RSU) for VII deployment. After analyzing the difference between traditional problems of locating traffic information detector and the problem of RSU location, a significance ranking model for RSU localization and three kinds of Significance Degree (SD) computing strategies are put forward. A VII simulation environment for the purpose of RSU localization optimization within VISSIM microscopic traffic simulation software is established developing add-on functions using VISSIM's Component Object Model (COM). A VII test bed of the Olympic Park network in Beijing is taken as an example to evaluate the performance of RSU localization model. The results of simulation experiments indicate that the mixed SD strategy considering both speed and route monitoring is superior to the other two SD strategies. Then, the impact of RSU number and OBE market penetration rate on the evaluation measures of traffic monitoring are studied with reference to the proposed mixed SD strategy. In this case, the evaluation measures of optimized RSU configurations generated by the ranking algorithm are always better than those of random RSU configurations. In addition, the benefits of optimized RSU configurations increase along with RSU number and market penetration rate while the benefits of random RSU configurations are more fluctuant.

Doppler Effect Analysis and Modulation Code Derivation

To reduce the risk of accidents, traffic, and other safety related problem's on public roads, the Wireless Access in Vehicular Environment (WAVE) standard was created. The WAVE also known as Dedicated Short Range Communications (DSRC) at 5.9GHz, is part of the Federal Highway Authority's Vehicle Infrastructure Integration (VII). This standard merely supports Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications for emerging Intelligent Transportation Systems (ITS). Due to the high mobility of this network, nodes involved in communication suffer from intermittent signal degradation. This is partly due to Doppler Effect (DE). This paper investigates and analyses the DE over wide Doppler Shift (DS) ranges. The results of the analysis clearly demonstrate that, sustainable communication links is achievable with DS ranging up to 1400Hz if an appropriate Modulation Code Scheme (MCS) can be selected. It also demonstrates that a BPSK Rate of 1/2 is not always a good candidate when combating DE.

Merging mobility and energy vision with hybrid electric vehicles and vehicle infrastructure integration

As the U.S. federal government is seeking useful applications of Vehicle-Infrastructure Integration (VII) and encouraging a greener and more efficient automobile industry, this paper demonstrated a path to meet the national transportation goal via VII. An impact study was conducted in a midsize U.S. metropolitan area on the potential of utilizing VII communication in Hybrid Electric Vehicle (HEV) operations by simulating a VII-enabled vehicle framework for both conventional HEV and Plug-in Hybrid Electric Vehicles (PHEV). The data collection and communication capability of the VII system allowed the prediction of speed profiles at the vehicle level with an average error rate of 13.2%. With the prediction, at the individual vehicle level, VII technology allowed PHEV and HEV to achieve additional benefits with an approximately 3% decrease in total energy consumption and emission. At the network level, the benefit–cost analysis indicated that the benefit–cost ratios for PHEV and HEV of the VII vehicle network exceed one at the fleet penetration rate of 20% and 30%, respectively. Our findings encourage to support public and private investments in VII infrastructure and its integration with HEV and PHEV in order to reap the increased energy savings from these vehicles.

Development of V-to-X systems in North America: The promise, the pitfalls and the prognosis

The development of V-to-X systems in North America is described with a focus on the services and applications that may appear, particularly in the United States. The scope of this article is therefore broad, and while it involves engineering the emphasis is on transportation applications from V-to-X; this dictates consideration of societal and institutional considerations. Different types of over-the-air interfaces are covered, followed by a description of the evolution of the Vehicle Infrastructure Integration program into IntelliDrive SM, and subsequently to the more generic term, connected vehicle next, vehicle-to-infrastructure and vehicle-to-vehicle components are covered. The vehicle-to-vehicle section describes an analysis that suggests that the USDOT path toward mandating Dedicated Short Range Communications transceivers on vehicles may be well-founded. Anticipated institutional arrangements in addition to research and deployment ideas for the vehicle and infrastructure are then covered in a section entitled “the short horizon”. Finally, the future of V-to-X in North America is discussed from “the long horizon” view.

Vehicle infrastructure integration system using vision sensors to prevent accidents in traffic flow

This study describes the development of a vision sensor for detecting shock waves which is one of the main factors of accidents in highway traffic flow. The major contributions of this research are development of vehicle tracking and detection of shock wave in saturated traffic. Moreover, realisation of a vehicle infrastructure integration (VII) system for providing arrival information of such propagation to drivers is proposed. The experiment on the analysis of the propagation with the developed image sensor has shown that an error might occur in the arrival time information of the propagation provided to the driver. Therefore a prediction technique at the arrival time of the propagation is integrated in the authors' system. By using this prediction technique and taking the error tolerance of drivers into account, the experimental results show that prediction success rates are improved by about 5%.

CyberCars-2 Cooperative Cybernetic Transport System Architecture

Research on Intelligent Cooperative Systems (ICS) based on wireless vehicular communication to improve automotive safety and traffic efficiency is being promoted through initiatives such as the Vehicle Safety Communications Consortium (VSCC) and Vehicle-Infrastructure Integration (VII) in the US, SMARTWAY and Vehicle Information and Communication System (VICS) in Japan, and European R&D activities within the framework of i2010: Intelligent Car Initiative - e.g., PReVENT, CVIS, and SAFESPOT. Related standardization activities include Continuous Air Interface for Long and Medium Range (CALM) to set up a stable, globally compatible regulation framework enabling higher market impact. The Car2Car Communication Consortium (C2C-CC) supports an open, interoperable industry standard for vehicular communication based on wireless local area network (WLAN) technologies pushing for specific frequency band allocation at 5.9 GHz for critical automotive safety. This paper shows CyberCars-2’s cooperative Cybernetic Transport System (CTS) architecture compatible with C2C-CC and CALM achievements. SAFESPOT for architecture design was adopted after meeting cooperative systems benefits, i.e., improving traffic safety for more efficient logistics and response to hazards and accidents, improved road network management and control and shorter, more predictable journey times.

Testbed for evaluation of vehicular detection systems

AbstractReliable and robust detection systems are the cornerstone in the efficient and proper functioning of vehicle infrastructure integration. This paper presents the design and implementation of a testbed for evaluating vehicular detection systems. The development of the testbed involved design of the hardware setup required for real‐time monitoring of the sensors, programming the devices for data capture, and development of algorithms to automate the analysis of the recorded data. The methodology used to calibrate and validate the algorithms to automate the analysis of the data is presented as well. Although the testbed is presented in the context of evaluation of video detection systems at signalized intersections, it can be used for evaluating any vehicular detection systems. Copyright © 2010 John Wiley & Sons, Ltd.

Estimation of Freeway Traffic Density with Loop Detector and Probe Vehicle Data

Density, speed, and flow are the three critical parameters for traffic analysis. High-performance traffic management and control require the estimation–prediction of space mean speed and density for large spatial and temporal coverage. Speed, including spot mean speed and space mean speed, and flow estimation are relatively easy to measure and estimate, while less attention has been devoted to measuring and estimating density. Because IntelliDrive (previously known as vehicle infrastructure integration) is a promising technology for providing a new type of real-time traffic data, and loop detector systems have already been widely deployed, this paper proposes a method to estimate freeway traffic density with both loop detector data and IntelliDrive-based probe vehicle data. The proposed method has been validated with Berkeley Highway Laboratory loop detector data combined with field-collected probe vehicle data in the first validation study and next-generation simulation video trajectory data in the second validation test. The algorithm can be used offline and in real time.

Energy and Emission Benefit Comparison of Stationary and In-Vehicle Advanced Driving Alert Systems

Automobiles powered by fossil fuels are one of the major contributors to both criteria pollutant and greenhouse gas—in particular, carbon dioxide (CO2)—emissions. Previous studies revealed that unnecessary acceleration and hard braking in response to sudden changes of traffic signals may cause a significant amount of wasted energy and increased emissions. Altering the behavior of drivers approaching signalized intersections potentially could reduce energy consumption and emissions from motor vehicles without increasing travel time or delay. Two types of advanced driving alert systems (ADAS) are proposed: stationary ADAS [based on roadside infrastructure, such as changeable message signs (CMSs)] and in-vehicle ADAS [driven by advanced communication technology, such as vehicle-infrastructure integration (VII)]. These systems can help drivers avoid hard braking at intersections by providing real-time information on traffic signal status. A state-of-the-art modal emissions model is used to evaluate and compare the effects of these two types of ADAS on the reduction of vehicle fuel consumption and CO2 emissions. A numerical analysis of a single vehicle shows that ADAS can help reduce vehicle fuel consumption and CO2 emissions by up to 40% in the tested hypothetical conditions. The benefits of these systems are further investigated in a traffic simulation environment with various levels of congestion and posted speed limits. The simulation results reveal that both CMS-based ADAS and VII-driven ADAS can provide fuel and CO2 savings; VII ADAS offer greater savings in most cases.

Accelerated incident detection across transportation networks using vehicle kinetics and support vector machine in cooperation with infrastructure agents

This study presents a framework for highway incident detection using vehicle kinetics, such as speed profile and lane changing behaviour, as envisioned in the vehicle-infrastructure integration (VII, also known as IntelliDrive) in which vehicles and infrastructure communicate to improve mobility and safety. This framework uses an in-vehicle intelligent module, based on a support vector machine (SVM), to determine the vehicle's travel experiences with autonomously generated kinetics data. Roadside infrastructure agents (also known as RSUs: roadside units) detect the incident by compiling travel experiences from several vehicles and comparing the aggregated results with the pre-selected threshold values. The authors developed this VII-SVM incident detection system on a previously calibrated and validated simulation network in rural Spartanburg, South Carolina and deployed it on an urban freeway network in Baltimore, Maryland to evaluate its transportability. The study found no significant differences in the detection performance between the original network and a new network that the VII-SVM system has not seen before. This demonstrated the feasibility of developing a generic VII-SVM system, applicable across transportation networks.

Peer-to-Peer-Based Publish/Subscribe Architecture for Advanced Infrastructure Systems

Civil engineers have introduced sophisticated sensing and control systems to better operate infrastructure systems, resulting in what are termed advanced infrastructure systems (AISs). Past AIS research has largely focused upon the sensors needed to collect data, as well as applications using this data to derive information to drive management/control decisions. Of equal importance to AIS success is the availability of an effective data delivery mechanism to ensure that data are effectively provided to applications. This research examined the challenge of data delivery in AISs. Following an analysis of AIS characteristics and a review of the state-of-the-practice in AIS data delivery, the paper presents a peer-to-peer-based publish/subscribe architecture developed by the research team to meet AIS data delivery requirements. Finally, a case study is presented in which the architecture is tailored for a vehicle-infrastructure integration AIS implementation. Simulation results of the implementation reveal that the proposed architecture provides significant scalability benefits, as compared to traditional data delivery approaches, providing up to a 50% reduction in message transmissions without increasing delivery times to unacceptable levels.