Showing papers on "Traffic simulation published in 2018"

Microscopic Traffic Simulation using SUMO

Abstract: Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.

Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

Abstract: Although car-following behavior is the core component of microscopic traffic simulation, intelligent transportation systems, and advanced driver assistance systems, the adequacy of the existing car-following models for Chinese drivers has not been investigated with real-world data yet. To address this gap, five representative car-following models were calibrated and evaluated for Shanghai drivers, using 2100 urban-expressway car-following periods extracted from the 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The models were calibrated for each of the 42 subject drivers, and their capabilities of predicting the drivers’ car-following behavior were evaluated. The results show that the intelligent driver model (IDM) has good transferability to model traffic situations not presented in calibration, and it performs best among the evaluated models. Compared to the Wiedemann 99 model used by VISSIM®, the IDM is easier to calibrate and demonstrates a better and more stable performance. These advantages justify its suitability for microscopic traffic simulation tools in Shanghai and likely in other regions of China. Additionally, considerable behavioral differences among different drivers were found, which demonstrates a need for archetypes of a variety of drivers to build a traffic mix in simulation. By comparing calibrated and observed values of the IDM parameters, this study found that (1) interpretable calibrated model parameters are linked with corresponding observable parameters in real world, but they are not necessarily numerically equivalent; and (2) parameters that can be measured in reality also need to be calibrated if better trajectory reproducing capability are to be achieved.

A Generative Model of Urban Activities from Cellular Data

Abstract: Activity-based travel demand models are becoming essential tools used in transportation planning and regional development scenario evaluation. They describe travel itineraries of individual travelers, namely, what activities they are participating in, when they perform these activities, and how they choose to travel to the activity locales. However, data collection for activity-based models is performed through travel surveys that are infrequent, expensive, and reflect the changes in transportation with significant delays. Thanks to the ubiquitous cell phone data, we see an opportunity to substantially complement these surveys with data extracted from network carrier mobile phone usage logs, such as call detail records (CDRs). In this paper, we develop input–output hidden Markov models to infer travelers’ activity patterns from CDRs. We apply the model to the data collected by a major network carrier serving millions of users in the San Francisco Bay Area. Our approach delivers an end-to-end actionable solution to the practitioners in the form of a modular and interpretable activity-based travel demand model. It is experimentally validated with three independent data sources: aggregated statistics from travel surveys, a set of collected ground truth activities, and the results of a traffic micro-simulation informed with the travel plans synthesized from the developed generative model.

A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation

Abstract: This paper proposes a combined usage of microscopic traffic simulation and Extreme Value Theory (EVT) for safety evaluation. Ten urban intersections in Fengxian District in Shanghai were selected in the study and three calibration strategies were applied to develop simulation models for each intersection: a base strategy with fundamental data input, a semi-calibration strategy adjusting driver behavior parameters based on Measures of Effectiveness (MOE), and a full-calibration strategy altering driver behavior parameters by both MOE and Measures of Safety (MOS). SSAM was used to extract simulated conflict data from vehicle trajectory files from VISSIM and video-based data collection was introduced to assist trained observers to collect field conflict data. EVT-based methods were then employed to model both simulated/field conflict data and derive the Estimated Annual Crash Frequency (EACF), used as Surrogate Safety Measures (SSM). PET was used for EVT measurement for three conflict types: crossing, rear-end, and lane change. EACFs based on three simulation calibration strategies were compared with field-based EACF, conventional SSM based on Traffic Conflict Techniques (TCT), and actual crash frequency, in terms of direct correlation, rank correlation, and prediction accuracy. The results showed that, MOS should be considered during simulation model calibration and EACF based on the full-calibration strategy appeared to be a better choice for simulation-based safety evaluation, compared to other candidate safety measures. In general, the combined usage of microscopic traffic simulation and EVT is a promising tool for safety evaluation.

Study of traffic flow characteristics using different vehicle-following models under mixed traffic conditions

Abstract: To understand the congestion problem and the occurrence of bottlenecks and to formulate solutions for it, a thorough study of vehicle-to-vehicle interactions is necessary. Car-following models replicate the behavior of a driver following another vehicle. These models are widely used in the development of traffic simulation models, and in analysis of safety and capacity. In India, traffic on roads is mixed in nature with wide variations in physical dimensions and other vehicular and traffic characteristics with loose lane discipline. In mixed traffic conditions, leader-follower vehicle types are not only car–car cases but also there are different combinations of vehicles (e.g. car-two wheeler, two wheeler-auto rickshaw, and heavy vehicle-two wheeler). The present study focuses on evaluation of different vehicle-following models under mixed traffic conditions. The car-following models such as Gipps, Intelligent Driver Model (IDM), Krauss Model and Das and Asundi were selected for this study. These m...

An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

Abstract: A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper. The procedure yields an unsupervised machine learning method. The algorithm generates a proximity matrix which contains a similarity measure. This matrix is then reordered with hierarchical clustering to achieve a graphically interpretable representation. It is shown how the resulting proximity matrix can be visually interpreted and how the variation of the methods' metaparameter reveals different insights into the data. The proposed method is able to cluster data from any data source. To demonstrate the methods' potential, multiple features derived from a traffic simulation are used in this paper. The knowledge of traffic scenario clusters is crucial to accelerate the validation process. The clue of the method is that scenario templates can be generated automatically from actual traffic situations. These templates can be employed in all stages of the development process. The results prove that the procedure is well suited for an automatic categorization of traffic scenarios. Diverse other applications can benefit from this work.

Understanding the Highway Safety Benefits of Different Approaches of Connected Vehicles in Reduced Visibility Conditions

Abstract: This study evaluated the effectiveness of connected vehicle (CV) technologies in adverse visibility conditions using microscopic traffic simulation. Traffic flow characteristics deteriorate signifi...

A generic multi-level framework for microscopic traffic simulation : Theory and an example case in modelling driver distraction

Abstract: Incorporation of more sophisticated human factors (HF) in mathematical models for driving behavior has become an increasingly popular and important research direction in the last few years. Such models enable us to simulate under which conditions perception errors and risk-taking lead to interactions that result in unsafe traffic conditions and ultimately accidents. In this paper, we present a generic multi-level microscopic traffic modelling and simulation framework that supports this important line of research. In this framework, the driving task is modeled in a multi-layered fashion. At the highest level, we have idealized (collision-free) models for car following and other driving tasks. These models typically contain HF parameters that exogenously “govern the human factor”, such as reaction time, sensitivities to stimuli, desired speed, etc. At the lowest level, we define HF variables (task demand and capacity, awareness) with which we maintain what the information processing costs are of performing driving tasks as well as non-driving related tasks such as distractions. We model these costs using so-called fundamental diagrams of task demand. In between, we define functions that govern the dynamics of the high-level HF parameters with these HF variables as inputs. When total task demand increases beyond task capacity, first awareness may deteriorate, where we use Endsley's three-level awareness construct to differentiate between effects on perception, comprehension, anticipation and reaction time. Secondly, drivers may adapt their response in line with Fullers risk allostasis theory to reduce risk to acceptable levels. This framework can be viewed as a meta model, that provides the analyst possibilities to combine and mix a wide variety of microscopic models for driving behavior at different levels of sophistication, depending on which HF are studied, and which phenomena need to be reproduced. We illustrate the framework with a distraction (rubbernecking) case. Our results show that the framework results in endogenous mechanisms for inter- and intra-driver differences in driving behavior and can generate multiple plausible HF mechanisms to explain the same observable traffic phenomena and congestion patterns that arise due to the distraction. We believe our framework can serve as a valuable tool in testing hypotheses related to the effects of HF on traffic efficiency and traffic safety in a systematic way for both the traffic flow and HF community.

Force-Driven Traffic Simulation for a Future Connected Autonomous Vehicle-Enabled Smart Transportation System

Abstract: Recent technology advances significantly push forward the development and the deployment of the concept of smart , such as smart community and smart city. Smart transportation is one of the core components in modern urbanization processes. Under this context, the connected autonomous vehicle (CAV) system presents a promising solution towards the enhanced traffic safety and mobility through state-of-the-art wireless communications and autonomous driving techniques. Being capable of collecting and transmitting real-time vehicle-specific, location-specific, and area-wide traffic information, it is believed that CAV-enabled transportation systems will revolutionize the existing understanding of network-wide traffic operations and reestablish traffic flow theory. This paper develops a new continuum dynamics model for the future CAV-enabled traffic system, realized by encapsulating mutually-coupled vehicle interactions using virtual internal and external forces. Leveraging Newton’s second law of motion, our model naturally preserves the traffic volume and automatically handles both the longitudinal and lateral traffic operations due to its 2-D nature, which sets us apart from the existing macroscopic traffic flow models. Our model can also be rolled back to handle the conventional traffic of human drivers, and the experiment shows that the model describes real-world traffic behavior well. Therefore, we consider the proposed model a complement and generalization of the existing traffic theory. We also develop a smoothed particle hydrodynamics-based numerical simulation and an interactive traffic visualization framework. By posing user-specified external constraints, our system allows users to visually understand the impact of different traffic operations interactively.

Connection of the SUMO Microscopic Traffic Simulator and the Unity 3D Game Engine to Evaluate V2X Communication-Based Systems.

Abstract: In-vehicle applications that are based on Vehicle-to-Everything (V2X) communication technologies need to be evaluated under lab-controlled conditions before performing field tests. The need for a tailored platform to perform specific research on the cooperative Advanced Driving Assistance System (ADAS) to assess the effect on driver behavior and driving performance motivated the development of a driver-centric traffic simulator that is built over a 3D graphics engine. The engine creates a driving situation as it communicates with a traffic simulator as a means to simulate real-life traffic scenarios. The TraCI as a Service (TraaS) library was implemented to perform the interaction between the driver-controlled vehicle and the Simulation of Urban MObility (SUMO). An extension of a previous version, this work improves simulation performance and realism by reducing computational demand and integrating a tailored scenario with the ADAS to be tested. The usability of the implemented simulation platform was evaluated by means of an experiment related to the efficiency of a Traffic Light Assistant (TLA), showing the analysis of the answer that 80% of the participants were satisfied with the simulator and the TLA system implemented.

Short-term Travel-time Prediction on Highway: A Review on Model-based Approach

Abstract: Emerging technologies provide a venue on which on-line traffic controls and management systems can be implemented. For such applications, having access to accurate predictions on travel-times are mandatory for their successful operations. Transportation engineers have developed numerous approaches including model-based approaches. The model-based approaches consider underlying traffic mechanisms and behaviors in developing the prediction procedures and they are logically intuitive unlike datadriven approaches. Because of this explanation power, the model-based approaches have been developed for the on-line control purposes. For departments of transportation (DOTs), it is still a challenge to choose a specific approach that meets their requirements. In efforts to develop a unique guideline for transportation engineers and decision makers when considering for implementing modelbased approaches for highways, this paper reviews model-based travel-time prediction approaches by classifying them into four categories according to the level of details involved in the model: Macroscopic, Mesoscopic, CA-based, and Microscopic. Then each method is evaluated from five main perspectives: Prediction range, Accuracy, Efficiency, Applicability, and Robustness. Finally, this paper concludes with evaluations of model-based approaches in general and discusses them in relation to data-driven approaches along with future research directions.

Overview Of Application Of Traffic Simulation Model

Abstract: Traffic simulation is a widely used method applied in the research on traffic modelling, planning and development of traffic networks and systems. From the literature study, a variety traffic simulation models were found in experiments and applications with aims to imaginary real traffic operations. The traffic simulation models can be categorised into three namely, microscopic modelling, macroscopic modelling and mesoscopic modelling. This report is aimed to overview these traffic simulation models, in term of its function, limitation and application.

Comparison between ad-hoc demand responsive and conventional transit: a simulation study

Abstract: Considering the sprawl of cities, conventional public transport with fixed route and fixed schedule becomes less efficient and desirable every day. However, emerging technologies in computation and communication are facilitating more adaptive types of public transport systems, such as demand responsive transport that operates according to real-time demand. It is crucial to study the feasibility and advantages of these novel systems before implementation to prevent failure and financial loss. In this work, an extensive comparison of demand responsive transport and conventional public transport is provided by incorporating a dynamic routing algorithm into an agent-based traffic simulation. The results show that replacing conventional public transport with demand responsive transport will improve the mobility by decreasing the perceived travel time by passengers without any extra cost under certain circumstances. The simulation results are confirmed for different forms of networks, including a real-world network proving the potential of demand responsive transport to solve the challenge of underutilised conventional public transport in suburban areas with low transport demand.

Vehicle-In-the-Loop Test Environment for Autonomous Driving with Microscopic Traffic Simulation

Abstract: In our days, vehicle automation is in a continuous evolutionary phase consisting of experiments, testing and validation. Accelerating the development and deployment of autonomous vehicles and infrastructure is a real demand as these technologies have a great potential to improve traffic safety and resolve road transport problems. The Vehicle-In-the-Loop testing is therefore indispensable throughout the development process. As a potential solution to this need, this paper introduces a new approach for test environment capable to simulate realistic traffic around the autonomous test vehicle. The test car therefore can be put into virtual transportation network by applying realtime microscopic traffic simulation, i.e., the method enables safe vehicle testing. The proposed test environment was proved with real world autonomous car.

Capability of Current Car-Following Models to Reproduce Vehicle Free-Flow Acceleration Dynamics

Abstract: Microscopic traffic simulation models are widely used to assess the impact of measures and technologies on the road transportation system. The assessment usually involves several measures of performance, such as overall traffic conditions, travel time, energy demand/fuel consumption, emissions, and safety. In doing so, it is usually assumed that traffic models are able to capture not only traffic dynamics but also vehicle dynamics (especially to compute energy/fuel consumption, emissions, and safety). However, this is not necessarily the case with the possibility of achieving unreliable outcomes when extrapolating from traffic to measures of performance related to the vehicle dynamics. The objective of the present paper is to assess the capability of existing car-following models to reproduce observed vehicle acceleration dynamics. A set of experiments was carried out in the Vehicle Emissions Laboratories of the European Commission Joint Research Centre in order to generate relevant data sets. These experiments are used to test the performance of three well-known car-following models. Although all models have been largely tested against their capability to correctly reproduce traffic dynamics, the findings raise concerns about their capability (and thus of the traffic models using them) to predict the effect on the microscopic vehicle dynamics and thus on emissions and energy/fuel consumption. The results of the present work can be considered valid beyond the analyzed car-following models, as simple acceleration rules are usually assumed in the vast majority of the traffic simulation frameworks. Consequently, it can be concluded that there is a number.

A Formal Approach for Modeling and Simulation of Human Car-Following Behavior

Abstract: Car-following is the activity of safely driving behind a leading vehicle. Traditional mathematical car-following models capture vehicle dynamics without considering human factors, such as driver distraction and the reaction delay. Consequently, the resultant model produces overly safe driving traces during simulation, which are unrealistic. Some recent work incorporate simplistic human factors, though model validation using experimental data is lacking. In this paper, we incorporate three distinct human factors in new compositional car-following model called modal car-following model, which is based on hybrid input output automata (HIOA). HIOA have been widely used for the specification and verification of cyber-physical systems. HIOA incorporate the modeling of the physical system combined with discrete mode switches, which is ideal for describing piece-wise continuous phenomena. Thus, HIOA models offer a succinct framework for the specification of car-following behavior. The human factors considered in our approach are estimation error (due to imperfect distance perception), reaction delay, and temporal anticipation. Two widely used car-following models called Intelligent Driver Model (IDM) and Full Velocity Difference Model (FVDM) are used for extension and comparison purpose. We evaluate the root mean square (rms) error of the following vehicle position using the traces obtained from human drives through different driving scenarios. The result shows that our model reduces the rms error in IDM and FVDM by up to 48.8% and 7.41%, respectively.

Hardware-in-the-Loop Testing of Connected and Automated Vehicle Applications: A Use Case for Queue-Aware Signalized Intersection Approach and Departure:

Abstract: Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptio...

Assessing the Impact of Connected and Automated Vehicles. A Freeway Scenario

Abstract: In the next decades, road transport will undergo a deep transformation with the advent of connected and automated vehicles (CAVs), which promise to drastically change the way we commute. CAVs hold significant potential to positively affect traffic flows, air pollution, energy use, productivity, comfort, and mobility. On the other hand, there is an increasing number of sources and reports highlighting potential problems that may arise with CAVs, such as, conservative driving (relaxed thresholds), problematic interaction with human-driven vehicles (inability to take decisions based on eye contact or body language) and increased traffic demand. Therefore, it is of high importance to assess vehicle automated functionalities in a case-study simulation. The scope of this paper is to present some preliminary results regarding the impact assessment of cooperative adaptive cruise control (CACC) on the case-study of the ring road of Antwerp, which is responsible for almost 50% of the traffic and pollution of the city. Scenarios with various penetration rates and traffic demands were simulated showing that coordination of vehicles may be needed to significantly reduce traffic congestion and energy use.

Headway distribution models of two-lane roads under mixed traffic conditions: a case study from India

Abstract: The time headway of vehicles is of fundamental importance in traffic engineering applications like capacity, level-of-service and safety studies. Further, the performance of traffic simulation depends on inputs into the simulation process and ‘accurate vehicle generation’ is critical in this context. Thus, it is important to define headway distribution pattern for the purpose of analyzing traffic and subsequently, taking infrastructure related decisions. In so far, majority of the researches on this subject are based on homogeneous traffic and effects of mixed traffic especially on two-lane roads are yet to be culminated. The present study, thus, aimed at investigating headway distributions on such roads under mixed traffic situation. Field study was conducted on two-lane highways in India that exhibits heterogeneity in its traffic composition. Contestant headway distribution models were evaluated and four distribution functions namely, log-logistic, lognormal, Pearson 5 and Pearson 6 were considered while modeling the headway data. The appropriate models were selected using a methodology based on K-S test and subsequent field validation. Log-logistic distribution was found appropriate at moderate flow whereas, at congested state of flow it was Pearson 5. However, at unstable flow nearing capacity, both, following and non-following components of headways were observed to follow different distributional characteristics. Nomographs are developed for calculating the headway probabilities at different flow levels considering the appropriate distribution models. ‘Probability of headway less than‘t’s’ increases with the flow rate and rate of such increase is considerably high for headways 7.5 s or more. This attributes to the fact that at heavy flow more vehicles are entrapped inside platoons and they move in following with shorter headways. Further, a comparison was made between the headway probabilities obtained in the current study and road segments that exhibit more or less homogeneous traffic. It was found that at moderate flow level proportion of shorter headways are considerably high under mixed traffic. The present paper demonstrates the effect of mixed traffic on distribution of time headways of two-lane roads. Presence of slower vehicles in such traffic leads to frequent formation of platoons, thereby, increases the risk taking behavior of drivers’ while overtaking. As a result, proportion of shorter headways increases resulting in highly skewed observations. Thus, the study proposed Log-logistic distribution under medium flow since it can model events which have ‘increased initial rate’ and Pearson 5 under heavy flow to model ‘highly skewed data’. The model outputs were accordingly compared with other studies and were found to explain the mixed traffic characteristics satisfactorily. The present study, thus, creates a starting point of further initiatives aimed at establishing a robust method of modeling headways on two-lane roads with mixed traffic.

A Simulation Study of Predicting Real-Time Conflict-Prone Traffic Conditions

Abstract: Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. In this paper, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e., VISSIM) and the corresponding traffic conflicts data generated by the surrogate safety assessment model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e., support vector machines, k-nearest neighbours, and random forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative of the real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e., 30 s, 1 min, 3 min, and 5 min) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of random forests with 5-min temporal aggregation in the classification results. However, attention should be given to the calibration and validation of the simulation software so as to acquire more realistic traffic data, resulting in more effective prediction of conflicts.

Modeling Driver Merging Behavior: A Repeated Game Theoretical Approach:

Abstract: Various lane-changing models have been developed for use within microscopic traffic simulation software to replicate driver merging behavior. An understanding of human driving behavior, which can b...

Real-Time Detection and Mitigation of DDoS Attacks in Intelligent Transportation Systems

Abstract: Vehicular network (VANET), a special type of ad-hoc network, provides communication infrastructure for vehicles and related parties, such as road side units (RSU). Secure communication concerns are becoming more prevalent with the increasing technology usage in transportation systems. One of the major objectives in VANET is maintaining the availability of the system. Distributed Denial of Service (DDoS) attack is one of the most popular attack types aiming at the availability of system. We consider the timely detection and mitigation of DDoS attacks to RSU in Intelligent Transportation Systems (ITS). A novel framework for detecting and mitigating low-rate DDoS attacks in ITS based on nonparametric statistical anomaly detection is proposed. Dealing with low-rate DDoS attacks is challenging since they can bypass traditional data filtering techniques while threatening the RSU availability due to their highly distributed nature. Extensive simulation results are presented for a real road scenario with the help of the SUMO traffic simulation software. The results show that our proposed method significantly outperforms two parametric methods for timely detection based on the Cumulative Sum (CUSUM) test, as well as the traditional data filtering approach in terms of average detection delay and false alarm rate.

A cellular automata model for traffic flow based on kinetics theory, vehicles capabilities and driver reactions

Abstract: In this paper, a reliable cellular automata model oriented to faithfully reproduce deceleration and acceleration according to realistic reactions of drivers, when vehicles with different deceleration capabilities are considered is presented. The model focuses on describing complex traffic phenomena by coding in its rules the basic mechanisms of drivers behavior, vehicles capabilities and kinetics, while preserving simplicity. In particular, vehicles kinetics is based on uniform accelerated motion, rather than in impulsive accelerated motion as in most existing CA models. Thus, the proposed model calculates in an analytic way three safe preserving distances to determine the best action a follower vehicle can take under a worst case scenario. Besides, the prediction analysis guarantees that under the proper assumptions, collision between vehicles may not happen at any future time. Simulations results indicate that all interactions of heterogeneous vehicles (i.e., car–truck, truck–car, car–car and truck–truck) are properly reproduced by the model. In addition, the model overcomes one of the major limitations of CA models for traffic modeling: the inability to perform smooth approach to slower or stopped vehicles. Moreover, the model is also capable of reproducing most empirical findings including the backward speed of the downstream front of the traffic jam, and different congested traffic patterns induced by a system with open boundary conditions with an on-ramp. Like most CA models, integer values are used to make the model run faster, which makes the proposed model suitable for real time traffic simulation of large networks.

Evaluation of a model predictive control framework for motorway traffic involving conventional and automated vehicles

Abstract: A Model Predictive Control (MPC) strategy for motorway traffic management, which takes into account both conventional control measures and control actions executed by vehicles equipped with Vehicle Automation and Communication Systems (VACS), is presented and evaluated using microscopic traffic simulation. A stretch of the motorway A20, which connects Rotterdam to Gouda in the Netherlands, is taken as a realistic test bed. In order to ensure the reliability of the application results, extensive speed and flow measurements, collected from the field, are used to calibrate the site’s microscopic traffic simulation model. The efficiency of the MPC framework, applied to this real sizable and complex network under realistic traffic conditions, is examined for different traffic conditions and different penetration rates of equipped vehicles. The adequacy of the control application when only VACS equipped vehicles are used as actuators, is also considered, and the related findings underline the significance of conventional control measures during a transition period or in case of increased future demand.

Application and Evaluation of the Reinforcement Learning Approach to Eco-Driving at Intersections under Infrastructure-to-Vehicle Communications

Abstract: Eco-driving behavior is able to improve vehicles’ fuel consumption efficiency and minimize exhaust emissions, especially with the presence of infrastructure-to-vehicle (I2V) communications for conn...