{"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}
{"title":"A many-to-many pick-up and delivery problem under stochastic battery depletion of electric vehicles","authors":"Merve İbiş Bozyel , Mehmet Soysal , Mustafa Çimen","doi":"10.1080/19427867.2023.2294185","DOIUrl":"10.1080/19427867.2023.2294185","url":null,"abstract":"<div><div>The study extends the traditional pick-up and delivery problems (PDPs) to address the specific challenges of urban logistics and electric vehicle (EV) adoption. These challenges include the limited range of EVs, energy consumption along the route, and uncertainty in traffic conditions. To overcome the limited range of EVs, the study includes battery swapping stations to ensure sufficient energy to complete delivery routes. Vehicle energy consumption is considered to reduce range anxiety and optimize energy use. The study also considers the unpredictability of traffic conditions that affect energy consumption and delivery schedules. To address these concerns, the study proposes an approximate Quadratic Chance-Constrained Mixed-Integer Programming (QC-MIP) model with a linear approximation, a constructive heuristic and a meta-heuristic. These quantitative models incorporate comprehensive EV energy estimation approaches, enabling more accurate energy predictions. The proposed approaches provide valuable insights and strategies for improving energy efficiency and delivery performance in urban logistics environments.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1287-1304"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138946009","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":"Motorized two-wheeler riders’ rear brake application in sudden hazardous event of animal crossing","authors":"Monik Gupta , Nagendra R. Velaga","doi":"10.1080/19427867.2023.2291227","DOIUrl":"10.1080/19427867.2023.2291227","url":null,"abstract":"<div><div>This study aims to quantify the riders’ performance in the sudden event of animal crossing using explanatory variables such as psychological riding conditions and socio demographic parameters. The participants were asked to ride four sessions on the motorized two-wheeler simulator in randomized order: 1) Base, 2) Distraction, 3) Time pressure, and 4) Distraction along with Time pressure. The rear brake application was classified into four groups using K-means clustering: No braking, Mild braking, Harsh braking, and Very Harsh braking. Further, a multinomial logistic regression model was developed to quantify the rear braking behavior. The results from the study revealed that the odds of applying the harsher brakes are only 0.32 times in comparison to mild braking when riders are distracted. Overall findings indicate that riders’ psychological conditions can alter rider’s behavior, and driver training focusing on several hazards can further help in improving road safety.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1268-1275"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138494246","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 the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms","authors":"Yixu He , Yang Liu , Lan Yang , Xiaobo Qu","doi":"10.1080/19427867.2024.2305018","DOIUrl":"10.1080/19427867.2024.2305018","url":null,"abstract":"<div><div>The application of deep reinforcement learning (DRL) techniques in intelligent transportation systems garners significant attention. In this field, reward function design is a crucial factor for DRL performance. Current research predominantly relies on a trial-and-error approach for designing reward functions, lacking mathematical support and necessitating extensive empirical experimentation. Our research uses vehicle velocity control as a case study, build training and test sets, and develop a DRL framework for speed control. This framework examines both single-objective and multi-objective optimization in reward function designs. In single-objective optimization, we introduce “expected optimal velocity” as an optimization objective and analyze how different reward functions affect performance, providing a mathematical perspective on optimizing reward functions. In multi-objective optimization, we propose a reward function design paradigm and validate its effectiveness. Our findings offer a versatile framework and theoretical guidance for developing and optimizing reward functions in DRL, particularly for intelligent transportation systems.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 10","pages":"Pages 1338-1352"},"PeriodicalIF":3.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139518426","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}