Xinmei Chen , Chen Dong , Siyu Tao , Qiyuan Peng , Jie Liu
{"title":"Integrated Transit-Oriented Development (TOD) with suburban rail network design problem for maximizing profits","authors":"Xinmei Chen , Chen Dong , Siyu Tao , Qiyuan Peng , Jie Liu","doi":"10.1080/19427867.2024.2304999","DOIUrl":"10.1080/19427867.2024.2304999","url":null,"abstract":"<div><div>Driven by the transit-oriented development (TOD) policy for rail stations, this study focuses on the network design of rapid suburban railways and the TOD zone design of corresponding suburban rail stations within metropolitan areas. A multi-level rail transit network was introduced, and the newly planned suburban rail network was considered as an expansion of the existing network to provide rapid services between urban and suburban areas. An integrated programming model was proposed to determine the physical and service routes of suburban rail network, the frequencies of each line, and the TOD plan along the selected rail stations. The integrated model is solved using the Adaptive Large Neighborhood Search (ALNS) algorithm. The effectiveness of the proposed model and methods is illustrated through numerical experiments, and several scenarios are designed for parameter analysis.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1318-1337"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Qualitatively and quantitatively explore injury severity of light motor vehicle drivers involved in heavy goods vehicle crashes","authors":"Fulu Wei , Peixiang Xu , Yongqing Guo , Zhenyu Wang","doi":"10.1080/19427867.2024.2306009","DOIUrl":"10.1080/19427867.2024.2306009","url":null,"abstract":"<div><div>Crashes involving heavy goods trucks (HGVs) are of significant concern as it poses a higher risk of fatality to light motor vehicles (LMVs). The study constructs three Deep Forest models with different Cascade structures to explore the relationship between HGV-LMV crash severity and risk factors. Based on the HGV-LMV crash data in Shandong province, China, motorcycles, electric vehicles, and sedans are defined as the LMV. According to the comparison results, the Deep Forest with Cascade LightGBM is significantly better. Through model interpretability tools, the study found that motorcycle and electric vehicle drivers aged 58 to 86, and LMV drivers with 1 to 3 years of driving experience are more likely suffering severity and fatal injury (SFI) in HGV-LMV crashes. And, disobey traffic sign, illegal turning, overtaking, changing lane, and crashes happened on non-motorway, national and provincial roads have an positive effect on SFI. </div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1353-1365"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139979650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qingchao Liu , Ruohan Yu , Yingfeng Cai , Long Chen
{"title":"Studying the predictability of crash risk caused by manual takeover of autonomous vehicles in mixed traffic flow","authors":"Qingchao Liu , Ruohan Yu , Yingfeng Cai , Long Chen","doi":"10.1080/19427867.2023.2279807","DOIUrl":"10.1080/19427867.2023.2279807","url":null,"abstract":"<div><div>This study explores how to reduce the cost of prediction as much as possible while ensuring the prediction accuracy of a real-time crash risk model. The extreme gradient enhancement (XGBoost) algorithm was used to predict the crash risk of autonomous vehicles in different sections of highway. The results show that the prediction performance of the model is the best when the threshold value is 0.05. Choosing two variables to predict can ensure high accuracy and simultaneously reduce the cost of prediction when the accuracy of crash risk prediction of the three sections can reach 73%, 62%, and 70%. However, when only one variable can be selected due to sensor or system failure, the speed difference between the takeover car and the front car can be chosen to achieve the greatest benefit. These findings could provide a reference for technicians to design safer and more economical autonomous vehicles.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1205-1223"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"GNN-based passenger request prediction","authors":"Aqsa Ashraf Makhdomi , Iqra Altaf Gillani","doi":"10.1080/19427867.2023.2283949","DOIUrl":"10.1080/19427867.2023.2283949","url":null,"abstract":"<div><div>Passenger request prediction is essential for operations planning, control, and management in ride-hailing platforms. While the demand prediction problem has been studied extensively, the Origin-Destination (OD) flow prediction of passengers has received less attention from the research community. This paper develops a Graph Neural Network (GNN) framework along with the Attention Mechanism to predict the OD flow of passengers. The proposed framework exploits various linear and non-linear dependencies that arise among requests originating from different locations and captures the repetition pattern and the contextual data of that place. Moreover, the optimal size of the grid cell that covers the road network and preserves the complexity and accuracy of the model is determined. Extensive simulations are conducted to examine the characteristics of our proposed approach and its various components. The results show the superior performance of our proposed model compared to the existing baselines.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1237-1251"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138575513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring differences in injury severity between occupant groups involved in fatal rear-end crashes: a correlated random parameter logit model with mean heterogeneity","authors":"Renteng Yuan , Xin Gu , Zhipeng Peng , Qiaojun Xiang","doi":"10.1080/19427867.2023.2292859","DOIUrl":"10.1080/19427867.2023.2292859","url":null,"abstract":"<div><div>Rear-end crashes are one of the most common crash types. Passenger cars involved in rear-end crashes frequently produce severe outcomes. However, no study investigated the differences in the injury severity of occupant groups when cars are involved as following and leading vehicles in rear-end crashes. Therefore, the focus of this investigation is to compare the key factors affecting the injury severity between the front- and rear-car occupant groups in rear-end crashes. First, data is extracted from the Fatality Analysis Reporting System (FARS) for two types of rear-end crashes, including passenger cars as rear-end and rear-ended vehicles. Significant injury severity difference between front- and rear-car occupant groups is found by conducting likelihood ratio test. Moreover, the front- and rear-car occupant groups are modeled by the correlated random parameter logit model with heterogeneity in means (CRPLHM) and the random parameter logit model with heterogeneity in means (RPLHM), respectively. This study provides an insightful knowledge of mechanism of occupant injury severity in rear-end crashes.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1276-1286"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138632264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Mintu Miah , Kate Kyung Hyun , Stephen P Mattingly
{"title":"A review of bike volume prediction studies","authors":"Md Mintu Miah , Kate Kyung Hyun , Stephen P Mattingly","doi":"10.1080/19427867.2024.2310831","DOIUrl":"10.1080/19427867.2024.2310831","url":null,"abstract":"<div><div>No previous research provided a comprehensive review of the bicycle volume estimation techniques assessing the current research gaps in data and modeling makes it challenging to understand the most effective and accurate strategies to estimate bicycle volumes. This article provides a detailed review of 58 studies published from 1996 to 2021. The review results indicate that conventional modeling approaches such as Linear regression, Negative Binomial, Poisson regressions, and a factor-up method represent the most popular econometric statistical models for bicycle volume estimation, while a decision tree is popular among machine-learning-based techniques due to its simplicity and ease of application, interpretation, and estimation with small data sets. In addition, Strava data, Socio-demographic variables, and bicycle facilities significantly contribute to the predictions. The study documents the current research gaps and recommends future research directions to improve data source evaluations, variable creations, modeling, and scalability/transferability advancements.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1406-1433"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139755916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhanced forecasting of online car-hailing demand using an improved empirical mode decomposition with long short-term memory neural network","authors":"Jiaming Liu , Xiaoya Tang , Haibin Liu","doi":"10.1080/19427867.2024.2313832","DOIUrl":"10.1080/19427867.2024.2313832","url":null,"abstract":"<div><div>The study on forecasting demand for online car-hailing holds substantial implications for both online car-hailing platforms and government agencies responsible for traffic management. This research proposes an enhanced Empirical Mode Decomposition Long-short Term Memory Neural Network (EMD-LSTM) model. EMD technique reduces noise and extracts stable intrinsic mode functions (IMF) from the original time series. Genetic algorithm is deployed to improve the K-Means clustering for determining optimal clusters. These sub time series serve as input for the prediction model, with combined results giving final predictions. Experimental data from Didi includes Haikou’s car-hailing orders from May to October 2017 and Beijing’s from January to May 2020. Results show improved EMD-LSTM reduces instability and captures characteristics better. Compared to unmodified EMD-LSTM, RMSE decreases by 3.50%, 6.81%, and 6.81% for the three datasets, and by 30.97%, 20%, and 9.24% respectively compared to single LSTM model.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1389-1405"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139756103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The paired pickup and delivery problem with profit in a two-echelon delivery system with multiple trucks and drones","authors":"Ebrahim Teimoury , Reza Rashid","doi":"10.1080/19427867.2023.2278855","DOIUrl":"10.1080/19427867.2023.2278855","url":null,"abstract":"<div><div>Recently researchers proposed truck and drone coordination to increase delivery efficiency and suggested various truck-drone routing problems. In this paper, we also focused on truck and drone coordination and introduced the paired pickup and delivery problem with profit in a two-echelon delivery system. To solve the problem, we propose a hybrid variable neighborhood search algorithm. For this algorithm, we adapted existing neighborhood search operators from the literature and considering the structure of the proposed problem, developed new neighborhood search operators. Also, we have carried out numerous computational experiments to evaluate the proposed solution methods’ performance, where the results show the efficiency of the proposed algorithms. The results highlight that in the paired pickup and delivery problem, for small values of drone operational costs, employing the two-echelon truck and drone routing system increases the profit by up to 5.6 percent in comparison to the vehicle routing system with drones.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1171-1187"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on shared parking allocation considering the heterogeneity of parking slot providers’ temporary parking demand","authors":"Qiaoru Li , Juanjuan Cheng , Liang Chen","doi":"10.1080/19427867.2024.2303225","DOIUrl":"10.1080/19427867.2024.2303225","url":null,"abstract":"<div><div>Studies on the allocation of parking demand during the sharing period primarily focus on public parking users, ignoring parking slot providers’ temporary parking demand with heterogeneity. Therefore, this paper takes the allocation of parking slot providers’ temporary parking demand as the research object and establishes a differentiated parking allocation (DPA) model to maximize the platform’s net profit. The model is solved using the ant colony optimization (ACO) algorithm and compared with the First-Come-First-Served (FCFS) algorithm. Then, the platform adopts differentiated or undifferentiated charge measures when charging for the parking slot providers’ temporary parking demand. The numerical analysis is performed to select three indicators for evaluation: 1) the utilization rate, 2) the net profit, and 3) the degree of time fragmentation. Results show that the ACO algorithm has an excellent optimization effect in allocating, and the differentiated allocation-undifferentiated charges for the parking slot providers’ temporary parking demand is feasible.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1305-1317"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139408981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Are men from Mars, women from Venus? Investigating the determinants behind the intention to use fully automated taxis","authors":"Yonghan Zhu , Marijn Janssen , Chengyan Pu","doi":"10.1080/19427867.2024.2310336","DOIUrl":"10.1080/19427867.2024.2310336","url":null,"abstract":"<div><div>Acceptance by customers is key to the success of shared autonomous vehicles (SAVs). However, only a small group of early technology-savvy customers currently use such vehicles, while the general population does not. Based on the Unified Theory of Acceptance and Use of Technology, Theory of Perceived Risk, and perceived threat of unemployment combined with knowledge of automated vehicles, this research develops an integrated model to investigate the determinants behind the intention to use fully automated taxis. Furthermore, it tested the differences between gender. Through the analysis of 539 samples, the findings showed that performance expectancy, effort expectancy, social influence, and knowledge of automated vehicles positively influence acceptance intention, while perceived safety risk and the perceived threat of unemployment were negatively related to behavioral intention. Moreover, effort expectancy, social influence, and perceived safety risk showed greater influence on females, while knowledge of automated vehicles exerted stronger effects on males.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1366-1377"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139756095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}