Transportation Research Part C-Emerging Technologies最新文献

{"title":"Optimizing public parking supply and pricing strategies in a competitive market with shared private parking services","authors":"Jing Wang ,&nbsp;Hua Wang ,&nbsp;Xiaoning Zhang ,&nbsp;Hua Hu ,&nbsp;Wei Peng","doi":"10.1016/j.trc.2024.104774","DOIUrl":"10.1016/j.trc.2024.104774","url":null,"abstract":"<div><p>Shared private parking spots (SPPSs) are believed to play a crucial role in balancing the surging parking demand and further alleviating the parking pressure of public carparks. Previous relevant studies mainly focused on public parking spot management without consideration of SPPSs. Additionally, limited attention has been paid to the competitive behavior of parking options between two markets in traffic dynamic analysis in the literature. This paper investigates the parking supply and pricing strategies for a linear urban transportation network with both public and shared private parking spots. Firstly, we derive a user equilibrium in terms of departure time and mode-and-parking choice, with sufficient public parking spot provision. Secondly, we explore how the lack of public parking spots pushes some commuters to rent SPPSs and develop a mixed user equilibrium with an internal balance between SPPSs’ market demand and supply in case of inadequate public parking spots. Then, we further investigate how public parking spot provision and public parking fees affect commuters’ mode choice and departure time choice. Thirdly, we propose three management schemes: optimal public parking fee, optimal public parking spot provision, and joint optimization of public parking fee and public parking spot provision. Numerical results show that setting a proper public parking fee and providing sufficient public parking spot provision can effectively reduce total system cost. Moreover, the joint optimization scheme can further enhance the system performance in comparison to the other two management schemes.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OCC-MP: A Max-Pressure framework to prioritize transit and high occupancy vehicles","authors":"Tanveer Ahmed ,&nbsp;Hao Liu ,&nbsp;Vikash V. Gayah","doi":"10.1016/j.trc.2024.104795","DOIUrl":"10.1016/j.trc.2024.104795","url":null,"abstract":"<div><p>Max-pressure (MP) is a decentralized adaptive traffic signal control approach that has been shown to maximize throughput for private vehicles. However, MP-based signal control algorithms do not differentiate the movement of transit vehicles from private vehicles or between high and single-occupancy private vehicles. Prioritizing the movement of transit or other high occupancy vehicles (HOVs) is vital to reduce congestion and improve the reliability and efficiency of transit operations. This study proposes OCC-MP: a novel MP-based algorithm that considers both vehicle queues and passenger occupancies in computing the weights of movements. By weighing movements with higher passenger occupancies more heavily, transit and other HOVs are implicitly provided with priority, while accounting for any negative impacts of that priority on single occupancy vehicles. And, unlike rule-based transit signal priority (TSP) strategies, OCC-MP more naturally also accommodates conflicting transit routes at a signalized intersection and facilitates their movement, even in mixed traffic without dedicated lanes. Simulations on a grid network under varying demands and transit configurations demonstrate the effectiveness of OCC-MP at providing TSP while simultaneously reducing the negative impact imparted onto lower occupancy private vehicles. Furthermore, OCC-MP is shown to have a larger stable region for demand compared to rule-based TSP strategies integrated into the MP framework. The performance of OCC-MP is also shown to be robust to errors in passenger occupancy information from transit vehicles and can be applied when passenger occupancies of private vehicles are not available. Finally, OCC-MP can be applied in a partially connected vehicle (CV) environment when a subset of vehicles is able to provide information to the signal controller, outperforming baseline methods at low CV penetration rates.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A conditional diffusion model for probabilistic estimation of traffic states at sensor-free locations","authors":"Da Lei ,&nbsp;Min Xu ,&nbsp;Shuaian Wang","doi":"10.1016/j.trc.2024.104798","DOIUrl":"10.1016/j.trc.2024.104798","url":null,"abstract":"<div><p>Transportation administrators and urban planners rely on accurate network-wide traffic state estimation to make well-informed decisions. However, due to insufficient sensor coverage, traffic state estimation at sensor-free locations (TSES) poses significant challenges for downstream network-wide traffic analysis. This is because direct observations are not available at these sensor-free locations. Most existing traffic state estimation (TSE) research focuses on inferring several unknown time points based on observed historical data using deterministic models. In contrast, TSES is to infer the entire unknown traffic time series of a given sensor-free node, thereby presenting high predictive difficulty, as we could not learn any historical traffic patterns locally. In this study, we introduce a novel probabilistic model — the conditional diffusion framework with spatio-temporal estimator (CDSTE) — to tackle the TSES problem. When dealing with TSES, deterministic models can only produce point value estimates, which may substantially deviate from the actual traffic states of sensor-free locations. To mitigate this, the proposed CDSTE integrates the conditional diffusion framework with cutting-edge spatio-temporal networks to extract the underlying dependencies in traffic states between sensor-free and sensor-equipped nodes. This integration enables reliable probabilistic traffic state estimations for sensor-free locations, which can be used to quantify the variability of estimations in TSES to support flexible and robust decision-making processes for traffic management and control. Extensive numerical experiments on real-world datasets demonstrate the superior performance of CDSTE for TSES over five widely-used baseline models.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A parking incentive allocation problem for ridesharing systems","authors":"Ngoc-Dai Nguyen ,&nbsp;Bernard Gendron ,&nbsp;Nadia Lahrichi","doi":"10.1016/j.trc.2024.104782","DOIUrl":"10.1016/j.trc.2024.104782","url":null,"abstract":"<div><p>Ridesharing occurs when people with similar schedules and itineraries travel together to reduce their commuting costs. In this paper, we study how parking spaces can be used to incentivize drivers to participate in ridesharing systems. We develop a Parking Incentive Allocation (PIA) system to promote and allocate parking lots to ridesharing drivers in a stochastic and dynamic environment. The optimization problem is formulated at each period as a multi-stage stochastic decision-dependent program. To overcome the complexity of the model, we propose one greedy policy, and three approximations including two stochastic policies and an expected-value policy. We evaluate the effectiveness of the four policies on the data generated from GPS information collected by the MTL Trajet project, which studies residents’ travel patterns throughout the city of Montreal. The computational results indicate that on average, the approximate policies can improve the total distance saving by more than 20% over various problem settings. Additionally, the results show that the performance of the PIA system is significantly influenced by the attractiveness of the parking incentive to drivers.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141959400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“School near workplace” or “school near home”: Which one is better for the morning commute with both individual and household travelers in Y-shaped networks?","authors":"Zhao-Rui Li,&nbsp;Xiao Han,&nbsp;Rui Jiang","doi":"10.1016/j.trc.2024.104793","DOIUrl":"10.1016/j.trc.2024.104793","url":null,"abstract":"<div><p>Household travelers typically need to coordinate their travel decisions in various aspects, such as destination and trip-timing, resulting in distinctive travel patterns compared to individual travelers. This paper investigates the morning commute problem in a “Y-shaped” network featuring “school near workplace” in consideration of both individual and household travelers and further understands the impact of school locations on the morning commute by comparing it with the “school near home” network. We analytically derive all equilibrium cases in the “school near workplace” network and classify them into three traffic patterns. We analyze the welfare effects of the staggering policy, finding that implementing the staggering policy in the “school near workplace” network can achieve a “win–win” scenario for individuals and household travelers in some equilibrium cases. Also, when the capacity ratio of school-constrained bottleneck to common-constrained bottleneck is below a threshold, which one is better (i.e., “school near workplace” or “school near home”) depends on the free-flow travel costs of the two networks. Locating the school “near workplace” is better than “near home” when the difference in the free-flow related costs for children between the two networks is not very large. Furthermore, we analyze the welfare effects of bottleneck expansion in the “school near workplace” network, finding that bottleneck expansion paradox may emerge when expanding the school-constrained bottleneck. Although properly designing the schedule gap can eliminate the paradox, a significant increase in the schedule gap may be needed to escape from the paradoxical cases when the common-constrained bottleneck capacity is slightly larger than the school-constrained bottleneck capacity. Our study sheds light on the importance of school locations in determining the performance of traffic management policies in the morning commute with both individual and household travelers.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry-aware car-following model construction: Theoretical modeling and empirical analysis on horizontal curves","authors":"Xun Yang ,&nbsp;Zhiyuan Liu ,&nbsp;Qixiu Cheng ,&nbsp;Pan Liu","doi":"10.1016/j.trc.2024.104772","DOIUrl":"10.1016/j.trc.2024.104772","url":null,"abstract":"<div><p>Road geometry significantly influences the physical forces acting on vehicles and the perceptual ability of drivers. Unfortunately, most available car-following models ignore the influence of complex road geographical features, such as curvatures and slopes and thereby lack scalability. To fill these gaps, this study presents a framework for the construction of a geometry-aware car-following model. Under the over-alignment assumption, car-following motion on horizontal curves was simplified into seven internal or adjacent car-following scenarios. Two novel alternative vehicle control modes (centralized and decentralized) for car-following motions on a horizontal route were proposed. The structured features of each scenario, considering both lateral and longitudinal information, were defined mathematically. Open-source data with trajectory records and road surface conditions on highways in Japan were collected and used as empirical data sources. First, we analyzed the theoretical proportion of traffic scenarios that conformed to the traditional car-following model for any horizontal route. Several properties of the car-following scenario proportion were proposed and proved. Both empirical statistics and theoretical estimations showed the existence of real-world sizable car-following scenarios that could not be handled by traditional models. Owing to their powerful ability to handle complex input features, machining learning and deep learning models were applied in car-following behavior modeling to make multistep predictions. With high computational efficiency, the results were compared with those of models with traditional inputs to demonstrate the effectiveness of the proposed approach.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24002936/pdfft?md5=03a816ca8ef1ed0ddb3f49a5894e10fc&pid=1-s2.0-S0968090X24002936-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)","authors":"Xiaolin He ,&nbsp;Riender Happee ,&nbsp;Meng Wang","doi":"10.1016/j.trc.2024.104751","DOIUrl":"10.1016/j.trc.2024.104751","url":null,"abstract":"<div><p>Perceived risk is crucial in designing trustworthy and acceptable vehicle automation systems. However, our understanding of perceived risk dynamics remains limited, and corresponding computational models are scarce. This study formulates a new computational perceived risk model based on potential collision avoidance difficulty (PCAD) for drivers of SAE Level 2 automated vehicles. PCAD quantifies task difficulty using the gap between the current velocity and the safe velocity region in 2D, and accounts for the minimal control effort (braking and/or steering) needed to avoid a potential collision, based on visual looming, behavioural uncertainties of neighbouring vehicles, imprecise control of the subject vehicle, and collision severity. The PCAD model predicts both continuous-time perceived risk and peak perceived risk per event. We analyse model properties both theoretically and empirically with two unique datasets: Datasets Merging and Obstacle Avoidance. The PCAD model generally outperforms three state-of-the-art models in terms of model error, detection rate, and the ability to accurately capture the tendencies of human drivers’ perceived risk, albeit at the cost of longer computation time. Our findings reveal that perceived risk varies with the position, velocity, and acceleration of the subject and neighbouring vehicles, and is influenced by uncertainties in their velocities.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24002729/pdfft?md5=7579de6d426e3a71ad1ef4cd6ae696bd&pid=1-s2.0-S0968090X24002729-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRRFNet: An adaptive traffic object detection method based on camera and radar radio frequency fusion","authors":"Wenbo Wang,&nbsp;Weibin Zhang","doi":"10.1016/j.trc.2024.104791","DOIUrl":"10.1016/j.trc.2024.104791","url":null,"abstract":"<div><p>A large number of studies have proved that camera and radar fusion is a useful and economical solution for traffic object detection. However, how to improve the reliability and robustness of fusion methods is still a huge challenge. In this paper, an adaptive traffic object detection method based on a camera and radar radio frequency Network (CRRFNet) is proposed, to solve the problem of robust and reliable traffic object detection in noisy or abnormal scenes. Firstly, two different deep convolution modules are designed for extracting features from the camera and radar; Secondly, the camera and radar features are catenated, and a deconvolution module is built for upsampling; Thirdly, the heatmap module is used to compress redundant channels. Finally, the objects in the Field of View (FoV) are predicted by location-based Non-Maximum Suppression (L-NMS). In addition, a data scrambling technique is proposed to alleviate the problem of overfitting to a single sensor by the fusion method. The existing Washington University Camera Radar (CRUW) dataset is improved and a new dataset named Camera-Radar Nanjing University of Science and Technology Version 1.0 (CRNJUST-v1.0) is collected to verify the proposed method. Experiments show that CRRFNet can detect objects by using the information of radar and camera at the same time, which is far more accurate than a single sensor method. Combined with the proposed data scrambling technology, CRRFNet shows excellent robustness that can effectively detect objects in the case of interference or single sensor failure.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strategizing sustainability and profitability in electric Mobility-as-a-Service (E-MaaS) ecosystems with carbon incentives: A multi-leader multi-follower game","authors":"Haoning Xi ,&nbsp;Mengjie Li ,&nbsp;David A. Hensher ,&nbsp;Chi Xie ,&nbsp;Ziyuan Gu ,&nbsp;Yuan Zheng","doi":"10.1016/j.trc.2024.104758","DOIUrl":"10.1016/j.trc.2024.104758","url":null,"abstract":"<div><p>Electric Mobility-as-a-Service (E-MaaS) emerges as a promising solution for environmentally-friendly mobility in the future, yet MaaS operators have been struggling to achieve profitability. We introduce a novel E-MaaS ecosystem where platforms can leverage carbon credits revenue from the government’s emissions reduction fund (ERF) by incentivizing travelers to choose more E-MaaS services, thereby reducing carbon emissions. In such an E-MaaS ecosystem, travelers can select either electric (E)-MaaS or traditional (T)-MaaS services and submit heterogeneous requests, such as distance, service time, tolerance for inconvenience, and travel delay budget, which are modeled as inputs. We propose a multi-leader multi-follower game (MLMFG) model where each leader (MaaS platform) competes to maximize its profits by making operational decisions such as pricing, EV acquisition ratio, and E(T)-MaaS bundle allocation while anticipating travelers’ participation levels. In response to the platforms’ decisions, each follower (traveler) aims to minimize her travel costs by determining the participation levels for E(T)-MaaS services via multiple MaaS platforms. We develop a customized alternating direction method of multipliers (ADMM) algorithm to solve the proposed MLMFG efficiently. Comprehensive numerical experiments based on real-life data in Australia demonstrate the convergence and robustness of the proposed ADMM algorithm. Further, experimental results reveal how factors such as market size, travel demand, ERF budget, subsidy rate, and unit price boundaries impact the profits and operational strategies of different MaaS platforms. Overall, the proposed MLMFG model for the E-MaaS ecosystem provides valuable insights for MaaS operators aiming to balance profitability with environmental responsibility, navigating a future where sustainability and profitability goals could converge.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How simple behavioural modifications can influence evacuation efficiency of crowds: Part 1. Decision making of individuals","authors":"Milad Haghani,&nbsp;Maziar Yazdani","doi":"10.1016/j.trc.2024.104763","DOIUrl":"10.1016/j.trc.2024.104763","url":null,"abstract":"<div><p>Crowded environments are inherently vulnerable to a range of risks, including earthquakes, fires, violent attacks, and terrorism. In such scenarios, every second counts in an evacuation, as it can significantly impact the number of lives saved. This paper introduces a novel approach to optimising crowd evacuation processes, focusing on behavioural modification rather than traditional methods such as mathematical optimisation models or architectural adjustments. We propose that by altering the behaviours of individuals within a crowd, overall system efficiency can be enhanced from within. We explore the effects of imparting simple, easily understandable strategies or instructions to individuals that can improve evacuation efficiency. The current work concentrates on how modifications in individual <em>decision-making—</em>namely, exit-choice and exit-choice-changing behaviour<em>—</em>can influence evacuation dynamics. We present the results of six major evacuation experiments, encompassing nearly 100 experimental scenarios and repetitions, which specifically investigate the effect of influencing exit choice and adaptation in exit-choice behaviour. The investigation revolves around three core questions: (a) the impact of effective strategies (b) the potential consequences of detrimental strategies, indicative of common misconceptions or poor advice, and (c) the influence of varying levels of strategy adoption, examining how system efficiency changes as more individuals embrace either beneficial or harmful strategies. The findings indicate that behavioural modification can substantially influence evacuation efficiency. Interestingly, the negative impact of poor strategies outweighs the benefits of effective ones. With respect to beneficial strategies, a significant increase in efficiency is observed at initial and intermediate levels of strategy adoption/uptake, suggesting that complete compliance is not necessary to enhance overall system performance. The benefit of influencing decision adaptation behaviour is considerably more tangible than influencing exit choice behaviour. These insights establish a novel perspective in evacuation safety. They lay a foundational framework for developing targeted public education and training programs based on empirical evidence. They highlight the importance of awareness and self-regulation among crowds, showcasing their potential to significantly increase both efficiency and safety in evacuation scenarios, potentially saving lives.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24002845/pdfft?md5=c4b9e6e8572e11758af16c72c7a664a1&pid=1-s2.0-S0968090X24002845-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}