Publications
92 results found
Zhu B, Hu S, Kaparias I, et al., 2024, Revealing the driving factors and mobility patterns of bike-sharing commuting demands for integrated public transport systems, Sustainable Cities and Society, Vol: 104, ISSN: 2210-6707
Bike-sharing for integrated public transport systems (BIPTS) offers an effective solution to the first- and last-mile problems. However, most existing studies have used overly simplified single-catchment area methods to identify BIPTS demands, and the driving factors and mobility patterns of BIPTS commuting demands have remained unclear. To fill this gap, a comprehensive framework for analyzing BIPTS commuting demands is developed. The proposed framework integrates a multi-catchment area method for precise BIPTS demands identification, the SHapley Additive exPlanations (SHAP) approach for uncovering driving factors, and a combination of dimensionality reduction and clustering techniques for discerning mobility patterns, complemented by a validation mechanism. A case study in Beijing demonstrates the efficacy of our multi-catchment areas method, which reduces misidentification of BIPTS demands by 48.6 %. Notably, for morning peak first-mile demands, the driving factors are the available bike density of cycling catchment area, the bikeability index, and the metro passenger inflow. Strong factors interactions are observed, stemming from an imbalance between BIPTS demands and infrastructure supply. Additionally, three distinct commuting patterns emerge, attributed to variations in feature contributions. These insights are crucial for enhancing the seamless integration of bike-sharing and public transport systems.
Hu S, Shu S, Chen Z, et al., 2024, Sustainable impact analysis of freight pooling strategies on city crowdsourcing logistics platform, Transportation Research Part D: Transport and Environment, Vol: 130, ISSN: 1361-9209
This study investigates the effects of freight pooling strategies on urban crowdsourcing logistics, focusing on economic, social, and environmental outcomes. Utilising a mixed-integer linear programming model with an adaptive large neighbourhood search algorithm, our goal is to optimise the cost-efficiency of the freight pooling system. Real-world delivery and driver trajectory data from a major Chinese crowdsourcing logistics platform, along with high-resolution vehicle telematics data, validate our model in five scenarios, each defined by distinct cost coefficients reflecting diverse stakeholder priorities. Results show potential for up to a 21.3% reduction in carbon emissions, a 28.3% decrease in truck activity spatial coverage, and a 7.6% increase in available drivers. However, deadheading trips, due to order consolidation into fewer vehicles without an increase in overall demand, could offset maximum carbon reduction benefits by 16.4%. Other effects on customers’ and drivers’ welfare are explored for a comprehensive quantitative assessment of freight pooling strategies’ sustainability benefits.
Chen Y, Hu S, Zheng Y, et al., 2024, Coordinated expansion planning of coupled power and transportation networks considering dynamic network equilibrium, Applied Energy, Vol: 360, ISSN: 0306-2619
As electric vehicles (EVs) rapidly proliferate, they intensify the demands on coupled power and transportation networks (CPTNs), leading to operational challenges such as congestion and overload. To address these challenges and enhance the dynamic performance of CPTNs, this paper proposes a coordinated expansion planning model based on a dynamic modeling method to support the upgrading of CPTNs (in distribution lines, distributed generators, chargers and roads). The spatial–temporal evolution and interaction of power flow in power distribution systems and traffic flow in transportation networks is incorporated into a dynamic network equilibrium, revealing the impact of network expansion on system performance. Uniquely, it replaces static link models with a novel point queue model to better track queuing and charging dynamics at fast charging stations. A scenario-based bi-level stochastic optimization model is formulated to determine the optimal coordinated expansion strategies, considering the diversity of electric and traffic demand scenarios. The optimization problem is solved using a designed marginal-cost-based particle swarm optimization algorithm. Case studies demonstrate the model's effectiveness in alleviating 95.89% traffic congestion and reducing 4.75% low voltage risk and 28.86% overload risk, marking a significant advancement in CPTN management.
Zhou B, Zhou Q, Hu S, et al., 2024, Cooperative Traffic Signal Control Using a Distributed Agent-Based Deep Reinforcement Learning With Incentive Communication, IEEE Transactions on Intelligent Transportation Systems, ISSN: 1524-9050
Deep Reinforcement Learning has shown some promise in dynamic traffic signal control by adapting to real-time traffic conditions. However, multi-intersection control presents challenges, primarily due to the need for efficient information exchange across increasing intersections, and the importance of spatiotemporal dynamics in traffic flows. Traditional methods often focus solely on spatial or temporal aspects, leading to suboptimal control strategies. This paper introduces a novel Multi-Agent Incentive Communication Deep Reinforcement Learning (MICDRL) method, designed for collaborative control across multiple intersections. MICDRL features an incentive communication mechanism, allowing agents to generate customized messages that influence other agents’ policies, thereby enhancing coordination and achieving globally optimal decisions. A key feature of MICDRL is its reliance on local information for message generation, effectively reducing communication overhead while ensuring collaboration. Additionally, MICDRL integrates a teammate module that leverages temporal data for predicting other agents’ actions, crucial for understanding collective dynamics and spatial environment characteristics. Empirical results show that MICDRL outperforms several state-of-the-art methods in metrics like queue length and throughput. Furthermore, we introduce a tailored three-layer Internet-of-Things architecture to enhance data collection and transmission.
Chen Y, Hu S, Xie S, et al., 2024, Optimal Dynamic Pricing of Fast Charging Stations Considering Bounded Rationality of Users and Market Regulation, IEEE Transactions on Smart Grid, ISSN: 1949-3053
Electric vehicles (EVs), as popular transportation carriers and flexible electric loads, couple both the power distribution networks and transportation networks. The pricing schemes of fast charging stations significantly affects the EVs’ on-tirp charging behaviors and the operations of coupled power and transportation networks. This interdisciplinary paper proposes an optimal dynamic pricing method for fast charging stations to boost charging network operator’ (CNO’s) profits and avoid excessive charging costs of EVs. Time-varying traffic flow and charging demands are generated by a dynamic traffic assignment simulation, where a boundedly rational dynamic user equilibrium model is presented to capture the cost sensitivity of EVs and gasoline vehicle users. Additionally, a market regulator supervises the CNO’s pricing, balancing the interests of the CNO and users via a regulation constraint. To address the optimal dynamic pricing issue, we propose a three-level Stackelberg game involving the distribution network operator, CNO, and users. The existence of a game equilibrium is assured by the fixed-point theory. A Gauss-Seidel iteration algorithm with inertia weight is designed to solve the pricing problem. The numerical results have corroborated the effectiveness of the proposed method, illuminating the effects of bounded rationality and market regulation on the CNO’s profits and users’ travel costs.
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, Vol: 72, Pages: 14002-14015, 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 article, 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 article.
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.
Liu W, Lian S, Fang X, et al., 2023, An open-source and experimentally guided CFD strategy for predicting air distribution in data centers with air-cooling, Building and Environment, Vol: 242, ISSN: 0360-1323
Data centers are generally over cooled to ensure the trouble free running. In a data center with air-cooling, it is crucial to investigate the air distribution and temperature field to aid the design, which ensures the stable operation with less energy consumption. Computational fluid dynamics (CFD) is perfect for such a mission, but the cost is high and the user is offered with little customization as commercial software are mostly adopted. Therefore, this study presented a systematic investigation on making use of open-source software, including OpenFOAM and paraView to realize geometry preparation, mesh generation, experimentally guided numerical setup and solution, and results visualization. A JAVA program was developed to ensure the case preparation and simulation in just one command. Self-adapted momentum sources were developed to realize the desired flow rate through the servers. The strategy was validated and demonstrated by a pilot data center from Alibaba cloud, Alibaba group. The developed solver predicted the air temperature in both cold and hot isles of a data center with mean error of 0.7 K. This work initiated a starting point for achieving automated CFD simulation of data centers with open-source tools.
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
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
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- Citations: 2
Xin H, Ye Y, Na X, et al., 2023, Sustainable Road Pothole Detection: A Crowdsourcing Based Multi-Sensors Fusion Approach, SUSTAINABILITY, Vol: 15
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- Citations: 1
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
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- Citations: 2
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 R, Tang Z, Hu Q, et al., 2023, Bi-Level Programming Approach for Optimal Charging Station Location and Traffic Assignment in Electric Autonomous Vehicle Systems, Pages: 6013-6018, ISSN: 2153-0009
The increasing popularity of electric autonomous vehicles (EAVs) underscores the need for expanded en-route charging infrastructures to effectively support the widespread adoption of EAVs. This paper presents a bi-level program model for optimizing the placement of charging stations and traffic assignments within EAV systems. The upper-level model formulates a modified p-median problem to minimize the total flow-weighted distance for EAVs seeking to charge services by strategically locating charging stations in the transportation network. The lower-level model tackles the traffic assignment problem, aiming to minimize travel costs for EAVs considering stochastic charging demands. To solve the bi-level model, a customized heuristic iterative algorithm incorporating the Frank-Wolfe method is developed. A case study is conducted to demonstrate the feasibility and effectiveness of the proposed model. Furthermore, a sensitivity analysis is carried out to investigate the impact of variations in EAV users' value of time on charging station deployment.
Huang X, Hu S, Wang W, et al., 2023, Identifying Critical Links in Urban Transportation Networks Based on Spatio-Temporal Dependency Learning, IEEE Transactions on Intelligent Transportation Systems, ISSN: 1524-9050
The urban transportation network is crucial for societal development, but it is prone to failures like congestion caused by accidents or disasters. In particular, often network-wide failure is the result of a series of cascading failures originating from a small set of individual links. To prevent such failures, it is essential to identify these critical links and take early action. However, most existing approaches in the literature for evaluating the importance of each link rely on manually designed metrics (e.g., the Network Robustness Index). These methods are time-consuming and not suitable for large-scale urban networks. Additionally, these metrics fail to accurately capture the dynamic traffic interactions influenced by vehicle movement. In this paper, we present a novel method for identifying critical links by learning effective traffic interaction representation (the spatio-temporal dependencies) among roads. By representing the network as an un-directed graph and abstracting the road links as the nodes, we introduce a temporal graph attention model to capture spatial and temporal dependence between nodes. This model combines a graph attention network and a long short-term memory neural network and produces an attention matrix, which represents traffic interactions among links. Furthermore, we propose a traffic influence propagation model to evaluate the influence of each link for the entire road network based on the traffic interaction representation. We rank the importance of links based on their influence and then identify the critical links. A real-world case study in the city of Hangzhou, China is conducted to test our method and we use the network efficiency ratio to quantify its performance. The results suggest that our method can effectively identify the critical links at different periods.
Hu J, Lian S, Hu S, et al., 2023, A CNN-based generative model for vehicle trajectory reconstruction in mixed traffic flow
With the breakthrough of connected and autonomous vehicles (CAVs) technology, vehicle trajectories can be collected by various sensors installed on CAVs continuously and sent to the traffic control center for operation and management. However, the trajectory data collected by CAVs may contain incomplete part owing to sensor limitation, thus hindering the data availability. To address this issue, we propose a CNN-based generative model for reconstructing multiple vehicle trajectories in multi-lane traffic scenarios using hybrid detection data from both CAVs and fixed sensors. Specifically, we generate missing trajectories using a Generative Adversarial Network (GAN) architecture with spatio-temporal features extracted by Convolutional Neural Networks (CNNs). The performance of the method is examined on a simulated arterial by assessing the mean absolute error (MAE) of the reconstructed data. The results indicate our method is robust even at a low CAV penetration rate.
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.
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
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- Citations: 2
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
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- Citations: 2
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
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- Citations: 5
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
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- Citations: 9
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
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- Citations: 11
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
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- Citations: 6
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.
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.
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.
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.
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
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