Pub Date : 2023-10-23DOI: 10.1080/15472450.2023.2270409
Bruno Machado, Amaro de Sousa, Carina Pimentel
AbstractRecently, more environmentally friendly urban logistics (UL) services have emerged based on the integration of freight deliveries into passenger bus networks to perform UL activities within cities. The aim is to reduce the number of combustion powered vehicles operating within cities, thus improving the city quality of life in terms of pollution, noise, traffic congestion etc. This paper addresses the operational planning of an UL service where freight is dropped by clients at bus hubs located outside the city center, transported by buses to one of their stops located in the city center, and delivered to the destination address by a last mile operator (LMO). To support the operational planning of the service covering the entire logistics process (from the reception of freight delivery requests until the delivery of the requests on their destination), five operational objectives are considered and, for each objective, an Integer Linear Programming (ILP) model is proposed. The objectives cover the perspectives of the bus network operator and of the LMO and some objectives address the robustness of the operational planning solutions to failures. Additionally, five operational planning cases of practical interest where two of the previous objectives are lexicographically optimized are also addressed including a description of how they are solved with the proposed ILP models. We demonstrate the merits of the different operational planning methods with different generated instances whose characteristics allow the assessment of the impact of different parameters on the results obtained by the proposed models when solved with a standard solver.Keywords: integration of passenger and freight transportationmathematical modelsoperational planningurban logistics Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was co-financed by the European Regional Development Fund (FEDER) through COMPETE 2020 (Operational Program for Competitiveness and Internationalization) through the project SOLFI - Urban logistics optimization system with integrated freight and passenger flows (POCI-01-0247-FEDER-039870). The work was also supported by the research unit Governance, Competitiveness and Public Policy (UIDB/04058/2020) and by Algoritmi Research Center (UIDB/00319/2020), funded by national funds through FCT.
{"title":"Operational planning of integrated urban freight logistics combining passenger and freight flows through mathematical programming","authors":"Bruno Machado, Amaro de Sousa, Carina Pimentel","doi":"10.1080/15472450.2023.2270409","DOIUrl":"https://doi.org/10.1080/15472450.2023.2270409","url":null,"abstract":"AbstractRecently, more environmentally friendly urban logistics (UL) services have emerged based on the integration of freight deliveries into passenger bus networks to perform UL activities within cities. The aim is to reduce the number of combustion powered vehicles operating within cities, thus improving the city quality of life in terms of pollution, noise, traffic congestion etc. This paper addresses the operational planning of an UL service where freight is dropped by clients at bus hubs located outside the city center, transported by buses to one of their stops located in the city center, and delivered to the destination address by a last mile operator (LMO). To support the operational planning of the service covering the entire logistics process (from the reception of freight delivery requests until the delivery of the requests on their destination), five operational objectives are considered and, for each objective, an Integer Linear Programming (ILP) model is proposed. The objectives cover the perspectives of the bus network operator and of the LMO and some objectives address the robustness of the operational planning solutions to failures. Additionally, five operational planning cases of practical interest where two of the previous objectives are lexicographically optimized are also addressed including a description of how they are solved with the proposed ILP models. We demonstrate the merits of the different operational planning methods with different generated instances whose characteristics allow the assessment of the impact of different parameters on the results obtained by the proposed models when solved with a standard solver.Keywords: integration of passenger and freight transportationmathematical modelsoperational planningurban logistics Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was co-financed by the European Regional Development Fund (FEDER) through COMPETE 2020 (Operational Program for Competitiveness and Internationalization) through the project SOLFI - Urban logistics optimization system with integrated freight and passenger flows (POCI-01-0247-FEDER-039870). The work was also supported by the research unit Governance, Competitiveness and Public Policy (UIDB/04058/2020) and by Algoritmi Research Center (UIDB/00319/2020), funded by national funds through FCT.","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"25 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135365929","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}
Pub Date : 2023-10-17DOI: 10.1080/15472450.2023.2270415
Kaitai Yang, Hanyi Yang, Lili Du
AbstractTraffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves’ turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow.Keywords: clusteringconnected vehiclemachine learningshockwavesmoothening AcknowledgmentsThis research is partially supported by the National Science Foundation awards CMMI-1901994, CMMI-2213459 and CNS-2124858. The authors would like to extend their gratitude to the reviewers and editor for their insightful comments, which have increased the quality of this paper.Authors’ contributionsThe authors confirm their contribution to the paper as follows: Dr. L. Du initiated this idea and supervised the whole study. Students K. Yang and Dr. H. Yang conducted the approach development, implementation, and data collection. All three authors drafted, edited, and reviewed the manuscript. They all reviewed the results and approved the final version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 This threshold is set offline based the traffic data in our experiments. Our approach is not very sensitive to this threshold. It can some values around 10 mph based on how you define the slow traffic in the applications.Additional informationFundingThis research is partially supported by the National Science Foundation awards CMMI-1901994, CMMI-2213459 and CNS-2124858. The authors would like to extend their gratitude to the reviewers and editor for
{"title":"A data-driven traffic shockwave speed detection approach based on vehicle trajectories data","authors":"Kaitai Yang, Hanyi Yang, Lili Du","doi":"10.1080/15472450.2023.2270415","DOIUrl":"https://doi.org/10.1080/15472450.2023.2270415","url":null,"abstract":"AbstractTraffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves’ turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow.Keywords: clusteringconnected vehiclemachine learningshockwavesmoothening AcknowledgmentsThis research is partially supported by the National Science Foundation awards CMMI-1901994, CMMI-2213459 and CNS-2124858. The authors would like to extend their gratitude to the reviewers and editor for their insightful comments, which have increased the quality of this paper.Authors’ contributionsThe authors confirm their contribution to the paper as follows: Dr. L. Du initiated this idea and supervised the whole study. Students K. Yang and Dr. H. Yang conducted the approach development, implementation, and data collection. All three authors drafted, edited, and reviewed the manuscript. They all reviewed the results and approved the final version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 This threshold is set offline based the traffic data in our experiments. Our approach is not very sensitive to this threshold. It can some values around 10 mph based on how you define the slow traffic in the applications.Additional informationFundingThis research is partially supported by the National Science Foundation awards CMMI-1901994, CMMI-2213459 and CNS-2124858. The authors would like to extend their gratitude to the reviewers and editor for","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135992970","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}
AbstractInfrastructure-based sensors provide a potentially promising solution to support the wide adoption of connected and automated vehicles (CAVs) technologies at an early stage. For connected vehicles with lower level of automation that do not have perception sensors, infrastructure sensors will significantly boost its capability to understand the driving context. Even if a full suite of sensors is available on a vehicle with higher level of automation, infrastructure sensors can support overcome the issues of occlusion and limited sensor range. To this end, a cooperative perception modeling framework is proposed in this manuscript. In particular, the modeling focus is placed on a key technical challenge, time delay in the cooperative perception process, which is of vital importance to the synchronization, perception, and localization modules. A constant turn-rate velocity (CTRV) model is firstly developed to estimate the future motion states of a vehicle. A delay compensation and fusion module is presented next, to compensate for the time delay due to the computing time and communication latency. Last but not the least, as the behavior of moving objects (i.e., vehicles, cyclists, and pedestrians) is nonlinear in both position and speed aspects, an unscented Kalman filter (UKF) algorithm is developed to improve object tracking accuracy considering communication time delay between the ego vehicle and infrastructure-based LiDAR sensors. Simulation experiments are performed to test the feasibility and evaluate the performance of the proposed algorithm, which shows satisfactory results.Keywords: cooperative perceptioninfrastructure sensorsobject trackingtime delayunscented Kalman filter Author contributionsThe authors confirm their contribution to the paper as follows: study conception and design: Chenxi Chen, Xianbiao Hu, Zhitong Huang; data collection: Chenxi Chen; analysis and interpretation of results: Chenxi Chen; draft manuscript preparation: Chenxi Chen, Qing Tang, Xianbiao Hu, Zhitong Huang. All authors reviewed the results and approved the final version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Infrastructure sensor-based cooperative perception for early stage connected and automated vehicle deployment","authors":"Chenxi Chen, Qing Tang, Xianbiao Hu, Zhitong Huang","doi":"10.1080/15472450.2023.2257596","DOIUrl":"https://doi.org/10.1080/15472450.2023.2257596","url":null,"abstract":"AbstractInfrastructure-based sensors provide a potentially promising solution to support the wide adoption of connected and automated vehicles (CAVs) technologies at an early stage. For connected vehicles with lower level of automation that do not have perception sensors, infrastructure sensors will significantly boost its capability to understand the driving context. Even if a full suite of sensors is available on a vehicle with higher level of automation, infrastructure sensors can support overcome the issues of occlusion and limited sensor range. To this end, a cooperative perception modeling framework is proposed in this manuscript. In particular, the modeling focus is placed on a key technical challenge, time delay in the cooperative perception process, which is of vital importance to the synchronization, perception, and localization modules. A constant turn-rate velocity (CTRV) model is firstly developed to estimate the future motion states of a vehicle. A delay compensation and fusion module is presented next, to compensate for the time delay due to the computing time and communication latency. Last but not the least, as the behavior of moving objects (i.e., vehicles, cyclists, and pedestrians) is nonlinear in both position and speed aspects, an unscented Kalman filter (UKF) algorithm is developed to improve object tracking accuracy considering communication time delay between the ego vehicle and infrastructure-based LiDAR sensors. Simulation experiments are performed to test the feasibility and evaluate the performance of the proposed algorithm, which shows satisfactory results.Keywords: cooperative perceptioninfrastructure sensorsobject trackingtime delayunscented Kalman filter Author contributionsThe authors confirm their contribution to the paper as follows: study conception and design: Chenxi Chen, Xianbiao Hu, Zhitong Huang; data collection: Chenxi Chen; analysis and interpretation of results: Chenxi Chen; draft manuscript preparation: Chenxi Chen, Qing Tang, Xianbiao Hu, Zhitong Huang. All authors reviewed the results and approved the final version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059629","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}
Pub Date : 2023-08-23DOI: 10.1080/15472450.2023.2247329
Hessam Arefkhani, Y. Shafahi
{"title":"Sensor location models with reliable optimal solution for the observation of origin–destination matrix and route flows","authors":"Hessam Arefkhani, Y. Shafahi","doi":"10.1080/15472450.2023.2247329","DOIUrl":"https://doi.org/10.1080/15472450.2023.2247329","url":null,"abstract":"","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"1 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89489841","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}
Pub Date : 2023-08-16DOI: 10.1080/15472450.2023.2246374
Santhanakrishnan Narayanan, J. S. Salanova Grau, Rodric Frederix, Athina Tympakianaki, A. Masegosa, C. Antoniou
{"title":"Modeling of shared mobility services - An approach in between aggregate four-step and disaggregate agent-based approaches for strategic transport planning","authors":"Santhanakrishnan Narayanan, J. S. Salanova Grau, Rodric Frederix, Athina Tympakianaki, A. Masegosa, C. Antoniou","doi":"10.1080/15472450.2023.2246374","DOIUrl":"https://doi.org/10.1080/15472450.2023.2246374","url":null,"abstract":"","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"82 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85954321","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}
Pub Date : 2023-08-08DOI: 10.1080/15472450.2023.2245327
Xiaobo Ma, Abolfazl Karimpour, Yao-Jan Wu
{"title":"Eliminating the impacts of traffic volume variation on before and after studies: a causal inference approach","authors":"Xiaobo Ma, Abolfazl Karimpour, Yao-Jan Wu","doi":"10.1080/15472450.2023.2245327","DOIUrl":"https://doi.org/10.1080/15472450.2023.2245327","url":null,"abstract":"","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"131 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84744954","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}
Pub Date : 2023-08-07DOI: 10.1080/15472450.2023.2227959
Zihao Wang, Roger Lloret-Batlle, Jianfeng Zheng, Henry X. Liu
{"title":"Adaptive green split optimization for traffic control with low penetration rate trajectory data","authors":"Zihao Wang, Roger Lloret-Batlle, Jianfeng Zheng, Henry X. Liu","doi":"10.1080/15472450.2023.2227959","DOIUrl":"https://doi.org/10.1080/15472450.2023.2227959","url":null,"abstract":"","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"16 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73973599","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}
Pub Date : 2023-08-04DOI: 10.1080/15472450.2023.2205020
Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.
全网层面的时空交通模式识别在数据驱动型智能交通系统（ITS）中发挥着重要作用，也是短期预测和基于场景的交通管理等应用的基础。交通文献中的常见做法是依靠众所周知的通用无监督机器学习方法（如 k-means、分层、光谱、DBSCAN），仅根据内部评估指数来选择最具代表性的结构和日类型数量。这些指标易于计算，但却有局限性，因为它们只能使用聚类数据集本身的信息。此外，聚类的质量最好还能通过外部验证标准、专家评估或在预期应用中的表现来证明。本文的主要贡献在于测试和比较了内部验证与外部验证标准的常见做法，后者以短期预测的应用为代表，短期预测也可作为更一般的交通管理应用的代表。与使用短期预测的外部评估相比，内部评估方法倾向于低估应用所需的代表性日类型的数量。此外，本文还研究了使用降维方法的影响。只需使用原始数据集维度的 0.1%，就能实现非常相似的聚类和预测性能，而且根据聚类方法的不同，计算成本最多可降低 20 倍。K 均值聚类和聚类聚类可能是最具扩展性的方法，使用的计算资源最多可减少 60 倍，而预测性能却与 p 中值聚类非常相似。
{"title":"Revealing representative day-types in transport networks using traffic data clustering","authors":"","doi":"10.1080/15472450.2023.2205020","DOIUrl":"10.1080/15472450.2023.2205020","url":null,"abstract":"<div><p>Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 5","pages":"Pages 695-718"},"PeriodicalIF":2.8,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75269919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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