International Journal of Transportation Science and Technology最新文献_第6页

Exploring intercity travel decision-making in a developing country: Insights from COVID-19 impacts in Iran 探索发展中国家的城际旅行决策：从2019冠状病毒病对伊朗的影响看

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-10-05 DOI: 10.1016/j.ijtst.2024.09.006

Mohammad Bakeri , Farshidreza Haghighi , Amir Izadi

This study examines the impact of COVID-19 on intercity travel mode choices in Mazandaran province, Iran, addressing critical gaps in understanding how pandemics affect travel behavior in developing countries. The research focuses on how socio-economic factors, perceived health risks, and travel time influence individuals’ choices of transportation modes during the pandemic. Using a stated preference (SP) survey method with 669 participants, the study assessed how concerns about virus transmission and adherence to health protocols shape travel decisions. Discrete choice modeling (DCM) was employed to predict travel mode shares between public transport and personal vehicles. The findings reveal that COVID-19 risk perception, socio-economic factors, and travel time significantly impact travel behavior. Specifically, heightened perceived risk of infection resulted in a 25% reduction in public transportation use, with individuals increasingly opting for personal vehicles. Additionally, strict adherence to health protocols, such as mask-wearing and vehicle cleaning, improved safety perception, leading to a 40% increase in confidence in public transport. The study also found that socio-economic factors like age, income, and education significantly shaped travel preferences. These insights provide valuable guidance for public health policymakers and transportation authorities to enhance the safety and management of intercity travel during ongoing and future pandemics.

本研究考察了2019冠状病毒病对伊朗马赞达兰省城际旅行方式选择的影响，填补了在理解大流行如何影响发展中国家旅行行为方面的关键空白。该研究侧重于大流行期间社会经济因素、感知健康风险和旅行时间如何影响个人对交通方式的选择。通过对669名参与者的陈述偏好（SP）调查方法，该研究评估了对病毒传播的担忧和对健康协议的遵守如何影响旅行决策。采用离散选择模型（DCM）预测公共交通与私家车的出行方式共享度。研究结果显示，COVID-19风险认知、社会经济因素和旅行时间显著影响旅行行为。具体而言，感知感染风险的增加导致公共交通工具的使用减少了25%，个人越来越多地选择私家车。此外，严格遵守卫生规程，如戴口罩和清洁车辆，提高了安全意识，导致对公共交通的信心增加了40%。该研究还发现，年龄、收入和教育等社会经济因素显著影响着人们的旅游偏好。这些见解为公共卫生决策者和交通主管部门在当前和未来大流行期间加强城际旅行的安全和管理提供了宝贵指导。

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引用次数: 0

Graph convolutional LSTM algorithm for real-time crash prediction on mountainous freeways 用于山区高速公路实时碰撞预测的图卷积 LSTM 算法

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-07-11 DOI: 10.1016/j.ijtst.2024.07.002

Yesihati Azati , Xuesong Wang , Mohammed Quddus , Xuefang Zhang

Accurate real-time traffic crash prediction is crucial for proactive traffic safety management. Currently, the majority of real-time models forecast crashes every 5 min to support different intelligent transportation systems. However, these intervals might be too short for practical use in manually implementing proactive traffic safety measures such as deploying traffic law enforcement and emergency rescue resources. Therefore, this study develops hourly crash prediction models to provide network operators with sufficient time to take measures in advance. A section of a mountainous freeway in Guizhou province is divided into homogeneous segments, with crash data, traffic operations data, and meteorological data being collected hourly. As the result is an imbalanced dataset of crash and non-crash instances, the training dataset is resampled using synthetic minority over-sampling technique (SMOTE) to address the issue. To fully capture the complex spatiotemporal relationships in the data and achieve high crash prediction accuracy, a graph convolutional network-long short-term memory (GCN-LSTM) model is constructed for the first time, combining a graph convolutional network (GCN) and long short-term memory (LSTM) neural network. For comparison purposes, LSTM, extreme gradient boosting (XGBoost), and logistic regression (LR) models are developed. The results show that the GCN-LSTM model outperforms other models in hourly traffic crash prediction, and the optimal prediction performance is achieved with the crash-to-non-crash ratio of 1:4. The GCN-LSTM method is found to effectively capture the complex spatiotemporal relationships in prediction data and to handle imbalanced traffic crash data.

准确的交通事故实时预测对主动交通安全管理至关重要。目前，大多数实时模型每5分钟预测一次碰撞，以支持不同的智能交通系统。但是，这些时间间隔可能太短，无法实际用于手动实施主动交通安全措施，例如部署交通执法和紧急救援资源。因此，本研究建立小时崩溃预测模型，为网络运营商提前采取措施提供充足的时间。贵州省的一段山地高速公路被划分为同质路段，每小时收集碰撞数据、交通运行数据和气象数据。由于结果是崩溃和非崩溃实例的不平衡数据集，因此使用合成少数过度采样技术（SMOTE）对训练数据集进行重新采样以解决这个问题。为了充分捕捉数据中复杂的时空关系，实现较高的碰撞预测精度，首次将图卷积网络（GCN）与长短期记忆（LSTM）神经网络相结合，构建了图卷积网络-长短期记忆（GCN-LSTM）模型。为了比较，我们开发了LSTM、极端梯度增强（XGBoost）和逻辑回归（LR）模型。结果表明，GCN-LSTM模型在小时交通碰撞预测方面优于其他模型，碰撞与非碰撞比为1:4时，预测性能最优。研究发现，GCN-LSTM方法能够有效地捕捉预测数据中复杂的时空关系，处理不平衡交通事故数据。

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引用次数: 0

Optimizing waste management and enhancing asphalt performance: A sustainable approach using discarded baby diapers and face masks 优化废物管理，提高沥青性能：利用废弃婴儿尿布和口罩的可持续方法

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-05-24 DOI: 10.1016/j.ijtst.2024.05.005

Muhammad Abbas Bangash , Arshad Hussain , Nangyaley Khan , Yanjun Qiu

This research introduces an innovative approach to address the management of waste baby diapers (BDs) and face masks (FMs) in order to mitigate environmental risks associated with the indiscriminate disposal of BD and FM waste. The study focuses on using BDs and FMs in road pavement hot mix asphalt (HMA) to enhance bitumen and aggregate performance, respectively. The approach involves the direct incorporation of 4% shredded BD during the bitumen melting process, while varying percentages of shredded FM (0%, 0.5%, 1%, and 1.5% relative to aggregate weight) are utilized as aggregate coating through a melting process. The inclusion of BD enhances the resistance of modified bitumen (MB) to permanent deformations under high temperatures compared to conventional bitumen. Simultaneously, the treatment of FM significantly improves the physical and mechanical attributes of the aggregates. The combination of 4% BD and 1.5% FM results in improved densification, fostering robust bonding between aggregates and asphalt paste. This enhancement leads to a 39% increase in stability, an 18% increase in indirect tensile strength (ITS), and a 27% reduction in permanent deformations. Notably, there is a remarkable 53% increase in resistance to rut depth and a 33% increase in resilient modulus. Ultimately, the implementation of 4% BD as a bitumen enhancer and 1.5% FM as an aggregate modifier demonstrates the potential to achieve waste reductions of 36% and 61%, respectively. This approach extends beyond pavement enhancement, contributing to the broader societal mitigation of adverse effects associated with BD and FM waste.

本研究介绍了一种创新的方法来处理废弃婴儿纸尿裤（BD）和口罩（FMs）的管理，以减轻与无差别处置BD和FM废物相关的环境风险。研究重点是在路面热混合沥青（HMA）中分别使用BDs和FMs来提高沥青和骨料的性能。该方法包括在沥青熔化过程中直接掺入4%的碎BD，而在熔化过程中，不同比例的碎FM（相对于骨料重量的0%、0.5%、1%和1.5%）被用作骨料涂层。与常规沥青相比，掺入BD增强了改性沥青（MB）在高温下抵抗永久变形的能力。同时，FM处理显著改善了骨料的物理力学性能。4% BD和1.5% FM的组合改善了致密性，促进了骨料与沥青膏体之间的牢固结合。这种增强导致稳定性提高39%，间接抗拉强度（ITS）提高18%，永久变形减少27%。值得注意的是，抗车辙深度增加了53%，弹性模量增加了33%。最终，采用4%的BD作为沥青增强剂，1.5%的FM作为骨料改性剂，可以分别实现36%和61%的废物减少。这种方法不仅仅是改善路面，还有助于在更广泛的社会范围内减轻与BD和FM废物相关的不利影响。

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引用次数: 0

Machine learning-based climate zoning and asphalt selection for pavement infrastructure under changing climate: A focused study of Ningxia, China 气候变化下基于机器学习的路面基础设施气候区划与沥青选择——以宁夏为例

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-10-22 DOI: 10.1016/j.ijtst.2024.10.001

Feipeng Xiao , Zhitao Zhang , Zichao Wu , Wentao He , Jin Li

Climate change poses significant challenges to the durability and performance of asphalt pavements. This study presents a comprehensive analysis of climatic factors in Ningxia, China, to establish a robust climate zoning framework for asphalt pavements. Utilizing machine learning techniques, specifically the fuzzy c-means (FCM) algorithm, three distinct climate zones within Ningxia were divided considering climatic features such as maximum temperature, minimum temperature, average temperature, maximum temperature difference, cumulative precipitation, and cumulative radiation. Based on the historical climate data and long-term pavement performance (LTPP) model, five asphalt performance grade (PG) zones were classified in Ningxia Province. Besides, six climate sub-zones, which integrated the asphalt PG zones into climate zones, provided a more refined strategy for the asphalt selection. The study also projected future climate scenarios using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset provided by the National Aeronautics and Space Administration (NASA) to assess the impact of climate change on asphalt selection in Ningxia. The significant changes in pavement temperature indicated the necessity to adapt asphalt pavement designs to future climate scenarios. Overall, this research contributed to the construction of more climate-resilient pavement infrastructures and provided an analysis framework for other regions facing similar climate-induced challenges.

气候变化对沥青路面的耐久性和性能提出了重大挑战。本研究对中国宁夏的气候因素进行了全面分析，以建立一个稳健的沥青路面气候区划框架。利用机器学习技术，特别是模糊c均值（FCM）算法，考虑最高温度、最低温度、平均温度、最大温差、累积降水和累积辐射等气候特征，将宁夏划分为三个不同的气候带。基于历史气候数据和长期路面性能（LTPP）模型，将宁夏沥青性能等级划分为5个等级。6个气候分区将沥青PG区整合到气候分区中，为沥青的选择提供了更精细的策略。该研究还利用美国国家航空航天局（NASA）提供的NASA地球交换全球每日缩减预测（nex - gdp - cmip6）数据集预测了未来的气候情景，以评估气候变化对宁夏沥青选择的影响。路面温度的显著变化表明沥青路面设计必须适应未来的气候情景。总体而言，该研究有助于建设更具气候适应性的路面基础设施，并为其他面临类似气候挑战的地区提供分析框架。

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引用次数: 0

An improved multi-objective method for the selection of driverless taxi site locations 一种改进的无人驾驶出租车站点选址多目标方法

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-10-19 DOI: 10.1016/j.ijtst.2024.10.007

Yaqin He, Yu Xiao, Jiehang Chen, Daobin Wang

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various point-of-interest types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The non-dominated sorting genetic algorithm II (NSGA-II) is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods for several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

为了加快无人驾驶出租车的大规模部署，推动自动驾驶产业的发展，研究无人驾驶出租车的综合停车和充电设施的位置已经成为城市交通中的一个重要问题。本研究采用渐进的“初步选择-筛选-最优选择”方法进行选址。首先，对各种兴趣点类型进行聚类，初步选择停车点。在此基础上，建立了需求点覆盖范围最大化、建设成本最小化、满足最大人口需求、需求点与候选点之间距离最小化的多目标选址模型。采用非支配排序遗传算法II （NSGA-II）求解多个Pareto最优解。根据运营商、用户和公共交通系统选择评价指标，估计出Pareto最优解，从而得到最终的定位解。在建模过程中对几个关键参数的计算方法进行了改进。选择区位潜力和区位影响系数来调节无人驾驶出租车的停车位数量。此外，根据实际路网和路径规划绘制的等时线代表候选点的服务范围。同时，引入基于实际路网的距离而不是欧氏距离来计算候选点之间的距离。最后，实例研究表明，该方法可使到达需求点的总初始行程时间减少64%，且不受运营调度的影响。

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引用次数: 0

Modeling urban resident travel satisfaction during the morning and the evening peak hours: A case study in Beijing 早晚高峰时段城市居民出行满意度建模——以北京市为例

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-05-31 DOI: 10.1016/j.ijtst.2024.05.006

Zeqian Jin , Xia Yang , Chen Li , Feng Jang Hwang

Understanding how various factors influence travel satisfaction can assist in traffic policy-making. In the study, it is aimed to develop innovative urban resident travel satisfaction evaluation models by building a comprehensive travel satisfaction evaluation index system and considering the asymmetric traffic flow and difference in travel time urgency during the morning and evening peak hours. Both the internal factors reflecting resident-related characteristics including socio-economic attributes and travel characteristics, and the external factors reflecting road-related characteristics including traffic facilities, road traffic conditions, traffic environments, and service levels are considered. Then, for the morning and evening peak hours, a structural equation model (SEM) to capture the intrinsic interactions between latent factors, and an ordered logit model (OLM) to describe the direct influencing factors of travel satisfaction considering its ordered nature are built respectively. Finally, the proposed models are examined with the travel survey data collected in the Yizhuang district of Beijing, China. The numerical results show that both the internal and external factors have significant impacts on travel satisfaction. The SEM models capture the interactions between latent variables such as the positive relation between traffic facilities and traffic environments. The OLM results show that most external factors except the satisfaction of the road obstacles have positive influences on travel satisfaction. The research findings provide a better understanding of the intrinsic interactions between latent variables and direct influencing factors of travel satisfaction and put forward guidance on how to improve travel satisfaction.

了解各种因素如何影响出行满意度，有助于制定交通政策。本研究旨在通过构建综合出行满意度评价指标体系，并考虑早晚高峰时段交通流不对称和出行时间紧迫性差异，开发创新的城市居民出行满意度评价模型。反映居民相关特征的内部因素包括社会经济属性和出行特征，反映道路相关特征的外部因素包括交通设施、道路交通条件、交通环境和服务水平。然后，针对早高峰和晚高峰时段，分别建立了捕捉潜在因素之间内在相互作用的结构方程模型（SEM）和考虑其有序性质的有序logit模型（OLM）来描述旅行满意度的直接影响因素。最后，以北京市亦庄地区的旅游调查数据为例，对所提出的模型进行了验证。数值结果表明，内部因素和外部因素对旅游满意度都有显著影响。SEM模型捕捉了潜在变量之间的相互作用，例如交通设施与交通环境之间的正相关关系。结果表明，除道路障碍物满意度外，大多数外部因素对出行满意度都有正向影响。研究结果有助于更好地理解旅游满意度的潜在变量与直接影响因素之间的内在相互作用，并为如何提高旅游满意度提供指导。

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引用次数: 0

Predicting hazard degree levels of metro operation accidents based on ordered constraint Apriori-RF method 基于有序约束 Apriori-RF 方法预测地铁运营事故的危险程度等级

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-07-03 DOI: 10.1016/j.ijtst.2024.06.008

Xiaobing Ding , Huilin Wan , Gan Shi , Chen Hong , Zhigang Liu

To explore the non-linear relationship between risk sources and the hazard degree levels of accidents, and to precisely predict the hazard impact of metro operation accidents, we propose the ordered constraint Apriori-RF method for forecasting metro operation accident hazard degree levels. First, the hazard degree of metro operation accidents is quantified from three dimensions: casualties, train delays, and facility damages. K-means clustering is then applied to categorize hazard degree levels. Second, the ordered constraint Apriori algorithm is employed to mine valid association rules between metro operation risk sources and accident hazard degree levels. These valid association rules are subsequently employed in the random forest (RF) algorithm for training, establishing a reliable and accurate prediction model. Finally, the method is validated using metro accident data from a city in China. The research results indicate that the ordered constraint Apriori-RF method enhances the effectiveness of association rule mining by 74.9% and exhibits higher computational efficiency. The predicted values of the ordered constraint Apriori-RF method have small errors. Compared to traditional RF algorithms, the root mean square error (RMSE) is reduced by 14%, and the weighted root mean square error (WRMSE) is reduced by 36%, demonstrating the higher accuracy of the ordered constraint Apriori-RF method and its clear advantages. The research findings provide a precise and effective method for quantitatively predicting the hazard degree levels of metro operation accidents, holding significant theoretical and practical value in ensuring metro operation safety and implementing accident mitigation and prevention measures.

为了探究风险来源与事故危害程度之间的非线性关系，准确预测地铁运营事故的危害影响，提出了有序约束Apriori-RF预测地铁运营事故危害程度的方法。首先，从人员伤亡、列车延误和设施损坏三个维度量化地铁运营事故的危害程度。然后应用k -均值聚类对危害程度等级进行分类。其次，利用有序约束Apriori算法挖掘地铁运营风险源与事故危害等级之间的有效关联规则；将这些有效的关联规则应用于随机森林（random forest， RF）算法中进行训练，建立可靠、准确的预测模型。最后，利用中国某城市地铁事故数据对该方法进行了验证。研究结果表明，有序约束Apriori-RF方法将关联规则挖掘的有效性提高了74.9%，具有较高的计算效率。有序约束Apriori-RF方法的预测值误差较小。与传统的射频算法相比，均方根误差（RMSE）降低了14%，加权均方根误差（WRMSE）降低了36%，表明有序约束Apriori-RF方法具有更高的精度和明显的优势。研究成果为地铁运营事故危险性等级的定量预测提供了精确有效的方法，对保障地铁运营安全、实施事故缓解和预防措施具有重要的理论和实践价值。

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引用次数: 0

Understanding the role of the COVID-19 pandemic on risky driving behavior and injury severity of drivers: Embracing heterogeneity in means and variances 了解COVID-19大流行对驾驶员危险驾驶行为和伤害严重程度的影响：接受均值和方差的异质性

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-10-05 DOI: 10.1016/j.ijtst.2024.09.002

Sukallyan Ghosh , Salvador Hernandez , Nabeel Saleem Saad Al-Bdairi

The onset of the COVID-19 pandemic significantly altered global mobility patterns, leading to a marked decrease in travel activities worldwide. In the United States, travel demand fell notably, contributing to a 22% reduction in overall crashes in 2020 compared to the prior year. In Oregon, vehicle miles traveled (VMT) dropped by 10.8%, and crashes decreased by 23.9%, yet fatalities increased by 2.63%. This rise in fatal crashes is linked to altered driving behavior, including aggressive, distracted, and impaired driving. This study investigates factors related to risky driving behavior-induced crashes in Oregon during the pandemic. Utilizing a random parameter multinomial logit model that accommodates heterogeneity, we found significant correlations between reckless behaviors, such as driving without a license, speeding, and neglecting to use restraints, and the severity of injuries. Our findings indicate temporal instability in factors contributing to injury severity. In 2019, severe injuries were more common in crashes involving drug use, drivers aged 45–54, and in speed zones of 45–55 mph (1 mph = 1.609 344 km/h). In 2020, young drivers under 25 and night-time crashes on lit streets were more likely to result in severe injuries. This research sheds light on the impact of COVID-19 on driver behavior and injury severity, particularly concerning aggressive driving. The identified risk factors are crucial for state and federal agencies to enhance road safety measures and ensure safer environments for all road users.

COVID-19大流行的爆发显著改变了全球流动模式，导致全球旅行活动显著减少。在美国，旅行需求明显下降，导致2020年的总事故比上年减少了22%。在俄勒冈州，车辆行驶里程（VMT）下降了10.8%，交通事故下降了23.9%，但死亡人数上升了2.63%。致命车祸的增加与驾驶行为的改变有关，包括攻击性、分心和驾驶障碍。本研究调查了流行病期间俄勒冈州危险驾驶行为引发的撞车事故的相关因素。利用随机参数多项logit模型，我们发现鲁莽行为（如无证驾驶、超速驾驶和忽视使用约束）与伤害严重程度之间存在显著相关性。我们的研究结果表明，时间不稳定性是导致损伤严重程度的因素。2019年，严重伤害在涉及吸毒、45-54岁司机以及45-55英里/小时（1英里/小时= 1.609 344公里/小时）的交通事故中更为常见。2020年，25岁以下的年轻司机和夜间灯光街道上的撞车事故更有可能造成严重伤害。这项研究揭示了COVID-19对驾驶员行为和伤害严重程度的影响，特别是在攻击性驾驶方面。确定的风险因素对于州和联邦机构加强道路安全措施和确保所有道路使用者享有更安全的环境至关重要。

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引用次数: 0

An analytical model of many-to-one carpool system performance under cost-based detour limits 基于成本的绕行限制下多对一拼车系统性能分析模型

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-05-31 DOI: 10.1016/j.ijtst.2024.05.007

Xin Dong, Hao Liu, Vikash V. Gayah

Carpooling has emerged as a highly efficient method for mitigating traffic congestion. By strategically consolidating multiple travelers into fewer vehicles, carpooling substantially cuts down the overall number of vehicles on the road. However, the effectiveness of a carpooling system highly depends on the proportion of interested users who can be successfully matched and the amount of benefits users gain from these matches. This paper develops analytical models to estimate these metrics for a carpooling system that serves a many-to-one demand pattern, in which travelers share the same basic destination but travel from different origins. Two distinct scenarios are incorporated in the models: one where users have a preferred role as a driver or rider and another in which they are ambivalent between the two roles. The models provide the system’s expected match rate and average user surplus as a function of the network size, number of users, and travel costs. Different from previous studies, the proposed models developed here consider that users only participate in trips beneficial to them from a cost perspective, rather than assuming fixed detours. This allows for matching incorporating spatial and financial considerations, promising flexible and rational matches in carpool systems. Simulation tests are used to validate the effectiveness of the analytical models. Results also offer insights into how various factors impact the system’s performance.

拼车已经成为缓解交通拥堵的一种高效方法。通过战略性地将多名乘客整合到更少的车辆中，拼车大大减少了道路上的车辆总数。然而，拼车系统的有效性在很大程度上取决于能够成功匹配的感兴趣用户的比例以及用户从这些匹配中获得的收益。本文开发了分析模型来估计拼车系统的这些指标，该系统服务于多对一的需求模式，在这种模式下，旅行者共享相同的基本目的地，但从不同的起点出发。模型中包含了两种不同的场景：一种是用户喜欢扮演司机或乘客的角色，另一种是用户在这两种角色之间摇摆不定。这些模型将系统的预期匹配率和平均用户剩余作为网络规模、用户数量和旅行成本的函数。与以往的研究不同，本文提出的模型从成本角度考虑用户只参与对他们有利的行程，而不是假设固定的弯路。这使得匹配结合了空间和经济方面的考虑，在拼车系统中保证了灵活和合理的匹配。仿真试验验证了分析模型的有效性。结果还提供了对各种因素如何影响系统性能的见解。

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引用次数: 0

Assessing the impact of bicycle infrastructure on safety and operations using microsimulation and surrogate safety measures: A case study in Downtown Atlanta 使用微观模拟和替代安全措施评估自行车基础设施对安全和运营的影响：亚特兰大市中心案例研究

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2025-06-01 Epub Date: 2024-06-08 DOI: 10.1016/j.ijtst.2024.06.002

Katherine Lee , Amirarsalan Mehrara Molan , Anurag Pande , Uijeong Hwang , Subhrajit Guhathakurta , Mirabel Nkanor , Benedetta Sergio

This research assessed the impact of efficiently expanding the biking network in Atlanta, Georgia, using dedicated lanes for bicycles. A total of three different conditions, i.e., existing, proposed (by the authors), and alternative (suggested by the City of Atlanta) conditions, were modeled to see the effectiveness of bike infrastructure design improvement and expansion. Trajectory data collected from the VISSIM simulation model were used in the Federal Highway Administration (FHWA)’s surrogate safety assessment model (SSAM) to analyze the safety effect on the bike infrastructure improvement and expansion. Based on the results, both the proposed and alternative conditions resulted in safer travel through the network during the peak hour period without any apparent deterioration in delays. For instance, compared to the existing condition, the average stop delays decreased from 190 s to 164 s for the proposed and the alternative conditions. These findings showed that the introduction of bicycle lanes and narrower lanes for automobiles may not adversely affect the peak hour congestion. Also, fewer conflicts were observed in the simulated network of proposed and alternative conditions compared to existing conditions. Conflicts involving bicyclists were also reduced since the bicyclists can use their own lanes and do not have to interact with automobile traffic in the sharrows.

这项研究评估了在佐治亚州亚特兰大市使用自行车专用道有效扩展自行车网络的影响。总共有三种不同的条件，即现有的，建议的（作者）和替代的（由亚特兰大市建议的）条件，被建模来观察自行车基础设施设计改进和扩展的有效性。在美国联邦公路管理局（FHWA）的替代安全评估模型（SSAM）中使用VISSIM仿真模型收集的轨迹数据，分析自行车基础设施改进和扩建对安全的影响。根据结果，建议和替代条件都使高峰时段通过网络的旅行更加安全，而延误情况没有明显恶化。例如，与现有条件相比，建议条件和备选条件的平均停车延误时间从190秒减少到164秒。这些研究结果表明，引入自行车道和较窄的汽车道可能不会对高峰时段的拥堵产生不利影响。此外，与现有条件相比，在拟议条件和备选条件的模拟网络中观察到的冲突较少。由于骑自行车的人可以使用自己的车道，而不必在狭窄的道路上与汽车交通相互影响，因此涉及骑自行车者的冲突也减少了。

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引用次数: 0