83 results found
Zhou Q, Zhou B, Hu S, et al., 2023, A safety-enhanced eco-driving strategy for connected and autonomous vehicles: A hierarchical and distributed framework, Transportation Research Part C: Emerging Technologies, Vol: 156, ISSN: 0968-090X
This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs.
Xin H, Ye Y, Na X, et al., 2023, Sustainable Road Pothole Detection: A Crowdsourcing Based Multi-Sensors Fusion Approach, Sustainability (Switzerland), Vol: 15
Real-time road quality monitoring, involves using technologies to collect data on the conditions of the road, including information on potholes, cracks, and other defects. This information can help to improve safety for drivers and reduce costs associated with road damage. Traditional methods are time-consuming and expensive, leading to limited spatial coverage and delayed responses to road conditions. With the widespread use of smartphones and ubiquitous computing technologies, data can be collected from built-in sensors of mobile phones and in-vehicle video, on a large scale. This has raised the question of how these data can be used for road pothole detection and has significant practical relevance. Current methods either use acceleration sequence classification techniques, or image recognition techniques based on deep learning. However, accelerometer-based detection has limited coverage and is sensitive to the driving speed, while image recognition-based detection is highly affected by ambient light. To address these issues, this study proposes a method that utilizes the fusion of accelerometer data and in-vehicle video data, which is uploaded by the participating users. The preprocessed accelerometer data and intercepted video frames, were then encoded into real-valued vectors, and projected into the public space. A deep learning-based training approach was used to learn from the public space and identify road anomalies. Spatial density-based clustering was implemented in a multi-vehicle scenario, to improve reliability and optimize detection results. The performance of the model is evaluated with confusion matrix-based classification metrics. Real-world vehicle experiments are carried out, and the results demonstrate that the proposed method can improve accuracy by 6% compared to the traditional method. Consequently, the proposed method provides a novel approach for large-scale pavement anomaly detection.
Zhong S, Liu A, Jiang Y, et al., 2023, Energy and environmental impacts of shared autonomous vehicles under different pricing strategies, npj Urban Sustainability, Vol: 3
<jats:title>Abstract</jats:title><jats:p>The introduction of vehicle automation, shared mobility, and vehicle electrification will bring about changes in urban transportation, land use, energy, and the environment. The accurate estimation of these effects is therefore essential for sustainable urban development. However, existing research on estimating the energy and environmental effects of shared autonomous electric vehicles generally ignores the interaction between land-use and transportation systems. This study, therefore, analyzes the long-term effects of shared autonomous vehicles (SAVs) from the perspective of land use and transportation integration. Different SAV pricing scenarios are also developed to explore the optimal pricing strategy for low carbon–oriented SAVs. Moreover, the study has further assessed the effect of vehicle electrification on vehicle emissions and energy consumption. The results have shown a nonlinear relationship between SAV fares and their transportation, land-use, energy, and environmental effects. Under an appropriate pricing strategy, SAV deployment could reduce PM<jats:sub>2.5</jats:sub> emission and energy consumption by 56–64% and 53–61%, respectively. With the further introduction of vehicle electrification, these can rise to 76% and 74%.</jats:p>
Ling S, Yu Z, Cao S, et al., 2023, STHAN: Transportation Demand Forecasting with Compound Spatio-Temporal Relationships, ACM Transactions on Knowledge Discovery from Data, Vol: 17, ISSN: 1556-4681
Transportation demand forecasting is a critical precondition of optimal online transportation dispatch, which will greatly reduce drivers' wasted mileage and customers' waiting time, contributing to economic and environmental sustainability. Though various methods have been developed, the core spatio-temporal complexity remains challenging from three perspectives: (1) Compound spatial relationships. According to our empirical analysis, these relationships widely exist. Previous studies focus on capturing different spatial relationships using multi-homogeneous graphs. However, the information flow across various spatial relationships is not modeled explicitly. (2) Heterogeneity in spatial relationships. A region's neighbors under the same spatial relationship may have different weights for this region. Meanwhile, different relationships may also weigh differently. (3) Synchronicity between compound spatial relationships and temporal relationships. Previous research considers synchronous influences from spatial and temporal relationships in a homogeneous fashion while compound spatial relationships are not captured for this synchronicity.To address the aforementioned perspectives, we propose the Spatio-Temporal Heterogeneous graph Attention Network (STHAN), where the key intuition is capturing the compound spatial relationships via meta-paths explicitly. We first construct a spatio-temporal heterogeneous graph including multiple spatial relationships and temporal relationships and use meta-paths to depict compound spatial relationships. To capture the heterogeneity, we use hierarchical attention, which contains node level attention and meta-path level attention. The synchronicity between temporal relationships and spatial relationships, including compound ones, is modeled in meta-path-level attention. Our framework outperforms state-of-the-art models by reducing 6.58%, 4.57%, and 4.20% of WMAPE in experiments on three real-world datasets, respectively.
Liu J, Li J, Chen Y, et al., 2023, Multi-scale urban passenger transportation CO<inf>2</inf> emission calculation platform for smart mobility management, Applied Energy, Vol: 331, ISSN: 0306-2619
Passenger transportation is one of the primary sources of urban carbon emissions. Travel data acquisition and appropriate emission inventory availability make estimating high-resolution urban passenger transportation carbon emissions challenging. This paper aims to establish a method to estimate and analyze urban passenger transportation carbon emissions based on sparse trip trajectory data. First, a trip chain identification and reconstruction method is proposed to extract travelers' trip information from sparse trip trajectory data. Meanwhile, a city-scale trip sampling expansion method based on population and checkpoint data is proposed to estimate population movements. Second, the identified trip information (e.g., trip origin and destination, and travel modes) is used to calculate multimodal passenger transportation CO2 emissions based on a bottom-up CO2 emissions calculation approach. Third, we develop a multi-scale high-resolution transportation carbon emission calculation and monitoring platform and take the city of Hangzhou, one of China's leading cities, as our case study, with around 10 million daily trips data and a quarter million road links. Five modes of passenger transportation are identified, i.e., walking, cycling, buses, metro, and cars. Hourly carbon emissions are calculated and attributed to corresponding road links, which build up passenger transportation carbon emissions from road links to region and city levels. Results show that a typical working day's total passenger transportation CO2 emission is about 36,435 tonnes, equivalent to CO2 emissions from 4 million gallons of gasoline consumed. According to our analysis of the carbon emissions produced by approximately 40,000 km of roadways, urban expressways have the most hourly carbon emissions at 194 kg/(h·km). Moreover, potential applications of the developed methods and platform linking to smart mobility management (e.g., Mobility as a Service, MaaS) and how to work in tandem to supp
Hu Y, Yang P, Zhao M, et al., 2023, A Generic Approach to Eco-Driving of Connected Automated Vehicles in Mixed Urban Traffic and Heterogeneous Power Conditions, IEEE Transactions on Intelligent Transportation Systems, ISSN: 1524-9050
The connected automated vehicles (CAVs) are envisioned to be implemented most likely on electric vehicles, while traditional fuel-powered manually-driven vehicles (MVs) would probably still dominate the automobile market in the next decade. In this context, this paper addresses urban eco-driving of CAVs in mixed traffic and heterogeneous power conditions. The paper aims to develop a practical and deployable eco-driving strategy for CAVs in mixed traffic flow of CAVs and MVs under realistic and complex traffic conditions. Several typical eco-driving scenarios were studied in detail. In a nutshell, the eco-driving strategy for each CAV was determined by solving a typical two-point boundary value problem with minimum electric energy consumption in urban traffic conditions with small market penetration rates (MPRs) of CAVs. A rolling-horizon scheme was applied to implement the eco-driving strategy to handle uncertain/unpredictable disturbances of preceding MVs and the interference of junction queues to the eco-driving maneuvers of CAVs. The paper also studied how eco-driving for electrified CAVs would affect MVs’ fuel consumptions. Simulation studies were carried out on urban arterial roads of multiple signalized intersections in various scenarios of demand and MPR to verify the energy savings effect of the proposed eco-driving strategy. The results showed that via eco-driving electrified CAVs each had a potential of reducing energy consumption by 40%-61%, meanwhile leading to 5%-34% fuel savings on average for each following MV. Further issues concerning the energy saving mechanism of electrified CAVs, impacts of MVs cut-in from adjacent lanes, and passenger comfort were also examined.
Hu S, Ye Y, Hu Q, et al., 2023, A Federated Learning-Based Framework for Ride-sourcing Traffic Demand Prediction, IEEE Transactions on Vehicular Technology, ISSN: 0018-9545
Accurate short-term ride-sourcing demand prediction is vital for transportation operations, planning, and policy-making. With the models developed from data based on individual ride-sourcing companies to the joint models with data from multiple ride-sourcing companies, the prediction performance of the proposed models is enhanced significantly. However, the privacy issues of these models become a problem. Raw data collected from individual companies could cause business concerns and data privacy issues. In this paper, we propose a Federated Learning (FL) based framework for traffic demand prediction (FedTDP), to solve this problem without sacrificing the prediction performance. In our framework, the model can encapsulate the spatial and temporal correlation of traffic demand data via LSTM and GCN respectively. Moreover, by associating FL with the spatio-temporal model, no raw data is uploaded to the centralized server, and only model parameters are required. Furthermore, a Shapley value-based reward mechanism is proposed to evaluate the contribution of ride-sourcing companies and can be used as a means to distribute rewards accordingly. Finally, a real-world case study of Hangzhou City, China, is conducted. More than 16 million real-world ride-sourcing requests collected from 8 ride-sourcing companies are used, covering most of the ride-sourcing travel demand across the city. The case study shows that the FL-based spatio-temporal model outperforms several well-established prediction models while preserving data privacy. It demonstrates the effectiveness and potential of our proposed framework. Some discussions related to the real-world implementations of the Shapley value-based reward mechanism are also given in the paper.
Zhang Z, Su H, Yao W, et al., 2023, Uncovering the CO<inf>2</inf> emissions of vehicles: A well-to-wheel approach, Fundamental Research, ISSN: 2096-9457
Carbon dioxide (CO2) from road traffic is a non-negligible part of global greenhouse gas (GHG) emissions, and it is a challenge for the world today to accurately estimate road traffic CO2 emissions and formulate effective emission reduction policies. Current emission inventories for vehicles have either low-resolution, or limited coverage, and they have not adequately focused on the CO2 emission produced by new energy vehicles (NEV) considering fuel life cycle. To fill the research gap, this paper proposed a framework of a high-resolution well-to-wheel (WTW) CO2 emission estimation for a full sample of vehicles and revealed the unique CO2 emission characteristics of different categories of vehicles combined with vehicle behavior. Based on this, the spatiotemporal characteristics and influencing factors of CO2 emissions were analyzed with the geographical and temporal weighted regression (GTWR) model. Finally, the CO2 emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies. The results show that the distribution of vehicle CO2 emissions shows obvious heterogeneity in time, space, and vehicle category. By simply adjusting the existing NEV promotion policy, the emission reduction effect can be improved by 6.5%–13.5% under the same NEV penetration. If combined with changes in power generation structure, it can further release the emission reduction potential of NEVs, which can reduce the current CO2 emissions by 78.1% in the optimal scenario.
Wang Y, Yu X, Guo J, et al., 2022, Macroscopic traffic flow modelling of large-scale freeway networks with field data verification: State-of-the-art review, benchmarking framework, and case studies using METANET, Transportation Research Part C: Emerging Technologies, Vol: 145, ISSN: 0968-090X
Macroscopic traffic flow models are of paramount importance to traffic surveillance and control. Before their employments in applications, the models need to be calibrated and validated against real traffic data. The model calibration determines an optimal set of model parameters that minimizes the discrepancy between the modeling results and real traffic data. The model validation is furthermore performed to corroborate the accuracy of a calibrated model using data other than used for calibration. The model calibration aims to reflect traffic reality, while model validation focuses on the prediction of future traffic using calibrated models. This paper delivers a comprehensive review of state-of-the-art works on macroscopic model calibration and validation, proposes a benchmarking framework on traffic flow modeling, and has conducted a large number of case studies based on the framework using macroscopic traffic flow model METANET with respect to the urban expressway network in Shanghai. In comparison to previous works, quite more comprehensive results on model calibration have been presented in this paper, in consideration of congestion tracking, traffic flow inhomogeneity, capacity drop, stop-and-go waves, scattering, adverse weather conditions, and accidents. The paper has also reported many results of model validation with respect to the same field examples. The results demonstrate that METANET is able to model complex traffic flow dynamics in large-scale freeway networks with sufficient accuracy. The paper is closed with discussion on limitations and future works.
Li Y, Chen B, Zhao H, et al., 2022, A Car-Following Model for Connected and Automated Vehicles With Heterogeneous Time Delays Under Fixed and Switching Communication Topologies, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 23, Pages: 14846-14858, ISSN: 1524-9050
Yang H, Bao Y, Huo J, et al., 2022, Impact of road features on shared e-scooter trip volume: A study based on multiple membership multilevel model, Travel Behaviour and Society, Vol: 28, Pages: 204-213, ISSN: 2214-367X
E-scooter sharing systems have been widely adopted by cities around the world. Previous studies analyzed community-level factors influencing e-scooter usage. Few studies examined the effect of road features on e-scooter trip volume (ETV) of the road segment, which can reveal the road features that riders prefer. This study explores this topic by analyzing the ETV of 29,544 road segments in Calgary, Canada, while controlling for community-level factors. Because some segments are the boundaries of multiple communities, the multiple membership multilevel model is adopted to tackle this boundary problem. The results show that segments with sidewalks, dedicated bicycle facilities, lower speed limit, more street lights and trees have higher ETV. ETV is also higher in communities with high income, high percentage of commercial and residential area. Quantifying the effect of road features on ETV could help government agencies determine where e-scooters should be ridden and design road facility improvement plans for e-scooter users.
Li J, Xie N, Zhang K, et al., 2022, Network-scale traffic prediction via knowledge transfer and regional MFD analysis, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 141, ISSN: 0968-090X
Hu S, Zhou Q, Li J, et al., 2022, High Time-Resolution Queue Profile Estimation at Signalized Intersections Based on Extended Kalman Filtering, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 23, Pages: 21274-21290, ISSN: 1524-9050
Li Y, Lv Q, Zhu H, et al., 2022, Variable Time Headway Policy Based Platoon Control for Heterogeneous Connected Vehicles With External Disturbances, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 23, Pages: 21190-21200, ISSN: 1524-9050
Li Y, Zhong Z, Song Y, et al., 2022, Longitudinal Platoon Control of Connected Vehicles: Analysis and Verification, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 23, Pages: 4225-4235, ISSN: 1524-9050
Wang Y, Zhao M, Yu X, et al., 2022, Real-time joint traffic state and model parameter estimation on freeways with fixed sensors and connected vehicles: State-of-the-art overview, methods, and case studies, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 134, ISSN: 0968-090X
Liu W, Sun H, Lai D, et al., 2022, Performance of fast fluid dynamics with a semi-Lagrangian scheme and an implicit upwind scheme in simulating indoor/outdoor airflow, BUILDING AND ENVIRONMENT, Vol: 207, ISSN: 0360-1323
Shu S, Chen Z, Yu Z, et al., 2022, Modeling Freight-Sharing Platform Operations for Optimal Compensation Strategy Using Markov Decision Processes, Pages: 1006-1011
The urban freight-sharing is one special part of ride-sharing due to its characteristics corresponding to urban freight orders, including the high fragmentation of both demand and supply and geographic concentration. This study has applied a Markov Decision Process framework to determine the static and dynamic optimal compensation strategy offered to shippers and carriers, which aims to maximize the longterm accumulated expected discounted rewards for the freight-sharing platform. More specifically, with the incorporation of stochastic arrival of shippers and carriers, decisions of a shipper placing an order and a carrier accepting an order, the maximum amount of orders and carriers the platform could accommodate, and the current state of the platform regarding the number of unmatched orders and carriers, models are designed to give insights about the optimal compensation-settings under various scenarios with different supply and demand arrival rate. The developed models are tested with the real-world data.
Zhu Y, Li Y, Hu S, et al., 2022, Optimal Control for Vehicle Platoon Considering External Disturbances, Pages: 453-458
This article designs a novel controller that is composed of a linear quadratic regulator (LQR)-based controller and a sliding mode controller (SMC) to guarantee the consensus of vehicle platoon considering the effect of external disturbances. Particularly, a LQR-based controller is designed to ensure the consensus related to position, velocity and acceleration of vehicles in the platoon and the optimization of performance under a diversity of communication topologies. In addition, a SMC is used to deal with external disturbances. Under the LQR-based controller, the stability of the designed controller and the effects of communication topology are analyzed by the Lyapunov method. Finally, the simulation experiments illustrate the superiority of our proposal.
Ye A, Zhou Q, Liu X, et al., 2022, Modeling and Managing an On-Demand Meal Delivery System with Mixed Autonomy, Pages: 2007-2012
This paper investigates the on-demand meal delivery system with mixed autonomy. We have explored how the future implementation of autonomous vehicles (AVs) in the system will affects demand, the labor market of human couriers (HCs), and the service provider in the system. In the system, the service provider determines the fleet size of AVs, the average delivery price for customers, and the average hourly wage for HCs. In response to the operation and pricing strategies, customers decide whether or not to order meals with delivery services, and potential HCs decide whether or not to work for the system. Therefore, a market model is proposed to capture the interactions among the service provider, customers, and HCs. An adaptive particle swarm optimization (APSO) algorithm is adopted to find optimal solutions. The results of numerical experiments show that a lower cost of AVs leads to higher penetration of AVs, lowered delivery price, and improved service quality. As a result, expanded demand is expected. By comparing the market outcomes under a varying number of potential customers, we find that AVs are considered more cost-efficient in densely populated areas than HCs, and have a higher percentage in the mixed fleet. Henee, customers in those areas are served with improved quality of delivery services.
Zhang K, Li J, Zhou Q, et al., 2022, Short-Term Traffic Prediction with Balanced Domain Adaptation, Pages: 699-711
Short-term traffic forecasting has been a hot topic in the intelligent transportation systems field. The traditional traffic forecasting methods mostly fix traffic sensors. However, most sensors are subject to bad conditions, leading to noisy and insufficient raw data. Recent advances have provided new traffic prediction opportunities. For example, the transfer learning method takes advantage of data trained on one good dataset and transfers the knowledge to others with bad data. Existing applications do not consider the underlying data distributions sufficiently, limiting the prediction performance. We propose a transfer learning-based traffic flow prediction framework using the Balanced Domain Adaptation (BDA) method. Various regression models are fed into the framework to evaluate a good data source and predict bad target datasets. A case study using data from the Highways England is conducted. The results show that the proposed BDA-based framework can match the distributions between traffic flow datasets and significantly improve prediction accuracy.
Zhou C, Xiao D, Hu J, et al., 2022, An Example of Digital Twins for Bridge Monitoring and Maintenance: Preliminary Results, Pages: 1134-1143, ISBN: 9783030918767
In this study, we propose a digital twin pilot study for bridge monitoring and maintenance. In particular, an infrastructure management framework using UAV and surveillance cameras, and accelerometers-based digital twins is proposed to perform long-term and non-interruptive monitoring. Real-world monitoring data are obtained through an experimental test performed on the Juanhu bridge (Haning, Zhejiang, China). Traffic flow and accelerometer data of the tested bridge were measured. The digital twin model of the bridge is created as a real-time Finite Element model in OpenSees. The FE model geometry is produced using a 3D photogrammetric reconstruction, and its dynamic properties are updated based on Bayesian modal identification. The traffic flow information on the bridge is processed through computer vision techniques using the video footage from the UAV and surveillance cameras. The object detection algorithm YOLO and tracking algorithm DeepSORT are used to derive the time-space diagrams. These elements operate in tandem with the accelerometer data and the digital twin FE model to acquire a preliminary vehicle loading estimation. The results are presented in this study and showcase the feasibility of the proposed digital twin framework for bridge monitoring and maintenance.
Hu S, Shu S, Bishop J, et al., 2022, Vehicle telematics data for urban freight environmental impact analysis, Transportation Research Part D: Transport and Environment, Vol: 102, Pages: 103121-103121, ISSN: 1361-9209
Road freight transport is one of the major contributors to greenhouse gas and air pollutantemissions. Hence, it is increasingly regulated in urban areas to reduce its impact on theenvironment and human health. The rich data available from telematics has the potentialto provide high-resolution information, yet research has not been conducted to understand,evaluate, and ultimately improve the operation and impacts of urban road freight. This paperdemonstrates the role of vehicle telematics data in enabling quantitative assessment of theimpacts of urban freight transport for the effective management of relevant policies. We presenta comprehensive data-driven approach that provides a robust quantitative evaluation andapplies it to a case study of the London Lorry Control Scheme (LLCS) policy in UK. We showthat, for the studied freight operator, the LLCS policy affects their drivers’ route choice bothinside and outside the restricted hours. The spatio-temporal distributions of different parametersincluding traffic speeds, fuel economy and emissions at different times of the day are comparedand analyzed. The results indicate that the unintended consequences of urban freight transportpolicies can include an extra 15% vehicle-km traveled per trip and 12% liter of fuel consumedper trip
Chen C, Hu S, Ochieng WY, et al., 2021, Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach, JOURNAL OF ADVANCED TRANSPORTATION, Vol: 2021, ISSN: 0197-6729
Liu Q, Hu S, Angeloudis P, et al., 2021, Dynamic wireless power transfer system for electric-powered connected and autonomous vehicle on urban road network, IET INTELLIGENT TRANSPORT SYSTEMS, Vol: 15, Pages: 1153-1166, ISSN: 1751-956X
Tang C, Hu W, Hu S, et al., 2021, Urban traffic route guidance method with high adaptive learning ability under diverse traffic scenarios, IEEE Transactions on Intelligent Transportation Systems, Vol: 22, Pages: 2956-2968, ISSN: 1524-9050
With the rapid development of urbanization, the problem of urban traffic congestion has become increasingly prominent. Dynamic route guidance promises to improve the capacity of urban traffic management and mitigate traffic congestion in big cities. In the design of simulation-based experiments for most dynamic route guidance methods, the simulation data is generally estimated from a specific traffic scenario in the real-world. However, highly dynamic traffic in the city implies that traffic scenarios in real systems are diverse. Therefore, if a route guidance method cannot adjust its strategy according to the spatial and temporal characteristics of different traffic scenarios, then it cannot guarantee optimal results under all traffic scenarios. Thus, ideal dynamic route guidance methods should have a highly adaptive learning ability under diverse traffic scenarios so as to have extensive improvement capabilities for different traffic scenarios. In this study, an A* trajectory rejection method based on multi-agent reinforcement learning (A*R²) is proposed; the method integrates both system and user perspectives to mitigate traffic congestion and reduce travel time (TT) and travel distance (TD). First, owing to its adaptive learning ability, the A*R² can comprehensively analyze the traffic conditions for different traffic scenarios and intelligently evaluate the road congestion index from a system perspective. Then, the A*R² determines the routes for all vehicles from user perspective according to the road network congestion index. An extensive set of simulation experiments reveal that, under various traffic scenarios, the A*R² can rely on its adaptive learning ability to achieve better traffic efficiency. Moreover, even in cases where many drivers are not fully compliant with the route guidance, the traffic efficiency can still be improved significantly by A*R².
Yu J, Mo D, Xie N, et al., 2021, Exploring multi-homing behavior of ride-sourcing drivers via real-world multiple platforms data, TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, Vol: 80, Pages: 61-78, ISSN: 1369-8478
Qian G, Guo M, Zhang L, et al., 2021, Traffic scheduling and control in fully connected and automated networks, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 126, ISSN: 0968-090X
Wang Y, Yu X, Zhang S, et al., 2021, Freeway Traffic Control in Presence of Capacity Drop, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 22, Pages: 1497-1516, ISSN: 1524-9050
Hu Y, Wang Y, Jin X, et al., 2021, Urban Eco-driving of Connected and Automated Vehicles in Traffic-Mixed and Power-heterogeneous Conditions, IEEE Intelligent Transportation Systems Conference (ITSC), Publisher: IEEE, Pages: 873-878, ISSN: 2153-0009
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