International Conference on Models and Technologies for Intelligent Transportation Systems最新文献

{"title":"Calibration Procedure for Traffic Flow Models of Merge Bottlenecks","authors":"Felipe Augusto de Souza","doi":"10.1109/MTITS.2019.8883339","DOIUrl":"https://doi.org/10.1109/MTITS.2019.8883339","url":null,"abstract":"Travel times and delays on freeways are highly dependent on the discharge rate of the bottlenecks. Consequently, it is important to model the traffic flow at bottlenecks as accurate as possible. However this is not straightforward in merge bottlenecks as the traffic flow is impacted by the acceleration, deceleration, and lane changing maneuvers induced by the merging vehicles. The interaction of these factors results in a reduced discharge rate when the bottleneck is congested compared to the discharge rate observed when the bottleneck is uncongested. This phenomenon is often referred to as capacity drop. Therefore, a traffic flow model of merge areas must reproduce the capacity drop phenomenon features including: (i) magnitude of drop in the outflow, (ii) when and how the capacity drop occurs, and (iii) how and when the bottleneck can recover nominal capacity. This can be achieved by a model able to reproduce capacity drop and the correct imputation of its parameters. Here we tackle the imputation of parameters aspect by proposing a calibration procedure that ensures the aforementioned aspects of capacity drop are captured. The procedure, based on the Multi-Objective Differential Evolution (MODE), does not require any information about the calibrated model and therefore is applicable to different models. The output contains multiple solutions in contrast to the usual single solution in single-objective optimization. Therefore, it returns multiple combinations of parameters that can reproduce the field measurements with similar level of accuracy. Unlike single objective approach, defining weights is not necessary. This is beneficial even when the ultimate goal is to find a single solution. The practitioner can inspect the model outputs of each parameter set and pick the one that suits better. Also, the multiple solutions can be used for further analysis and applications such as parameter and output uncertainty. The procedure is tested against field data of a bottleneck in which capacity drop is consistently observed based on data of 16 days. The following implementations of link transmission model (LTM) are calibrated: (a) standard LTM with no extension (capacity drop is not captured), (b) LTM with outflow reduction based on the upstream queue (density); (c) LTM with outflow reduction based on on-ramp flow, and (d) LTM with outflow reduction based on on-ramp flow and queue. In all cases the algorithm output approximate the Pareto Frontier or trade-off curve between downstream outflow and density errors. As expected, the errors are smaller as additional features are added to the model (from case (a) - no feature - to case (d) - two features); however, among the models with one additional feature ((b) and (c)), considering ramp flows had lead to smaller errors. With the multiple solutions, time-dependent upper and lower bounds of density, outflow, and travel times can be obtained by applying the model for all solutions given expected demands. There","PeriodicalId":165866,"journal":{"name":"International Conference on Models and Technologies for Intelligent Transportation Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124367256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameter sampling strategies in traffic microsimulation","authors":"V. Punzo, M. Montanino","doi":"10.1109/MTITS.2015.7223235","DOIUrl":"https://doi.org/10.1109/MTITS.2015.7223235","url":null,"abstract":"The paper investigates the impact of different sampling strategies of car-following and lane-changing model parameters on traffic simulation results. The investigation considered seven possible sampling strategies including sampling parameters from independent normal distributions, which is customarily in commercial simulation software. Study results revealed that model performances in case of sampling from normal pdfs are extremely poor. In turn, results proved that parameter correlation, as well as the parameter distribution model, entail a big impact on model performances and should be properly take into account in the microsimulation practice.","PeriodicalId":165866,"journal":{"name":"International Conference on Models and Technologies for Intelligent Transportation Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116412274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who uses shared microbility? Exploring users' social characteristics beyond sociodemographics","authors":"Sophia Fuchs, David Duran-Rodas, M. Stöckle, Maximilian Pfertner","doi":"10.1109/MT-ITS49943.2021.9529285","DOIUrl":"https://doi.org/10.1109/MT-ITS49943.2021.9529285","url":null,"abstract":"Due to the current environmental and traffic-related problems of motorized individual transport (MIT), the importance of new, flexible, healthy, less pollutant, accessible mobility systems is growing. Bike and e-scooters have been shown to potentially mitigate these impacts. Therefore, we aim to explore the associated social characteristics of the users of bikes and e-scooters as shared options to identify the attributes of potential new customers and make them more competitive in the market. We explored social characteristics beyond the traditional sociodemographics, including psychographic, attitudinal, and behavioral attributes. These characteristics can help to understand deeper the interests, attitudes, and behavior of customers.Therefore, we conducted an online and offline survey in Munich, Germany with 408 respondents to evaluate who is using and who is not using bike sharing, shared e-scooters, and shared micromobility offers in general. Therefore shared micromobility user were classified as users of bike sharing and/ or shared e-scooter systems. The statistically significant parameters were then used to create classification models to predict users and non-users of shared micromobility. Results show that bike sharing is mainly used by high educated employed males with high income, who think that equity and adventure are important but not tradition. Bike sharing users feel that bikes are convenient, relaxing, and fun, which is not the case for private cars. They use bike sharing as well as public transport and other shared mobility options. Moreover, shared e-scooter users have values oriented to wealth and adventure but not tradition and they enjoy using other shared modes. Naive Bayes models helped to predict potential bike sharing users with an accuracy of 72% and shared e-scooter with 83%. The highest accuracy was scored by behavioral characteristics followed by sociodemographics and psychographic parameters.To the best of the authors’ knowledge, this would be one of the few studies on shared micromobility considering social characteristics beyond sociodemographics.","PeriodicalId":165866,"journal":{"name":"International Conference on Models and Technologies for Intelligent Transportation Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116602278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A low dimensional model for bike sharing demand forecasting","authors":"Guido Cantelmo, R. Kucharski, C. Antoniou","doi":"10.1109/MTITS.2019.8883283","DOIUrl":"https://doi.org/10.1109/MTITS.2019.8883283","url":null,"abstract":"Big, transport-related datasets are nowadays publicly available, which makes data-driven mobility analysis possible. Trips with their origins, destinations and travel times are collected in publicly available big databases, which allows for a deeper and richer understanding of mobility patterns.This paper proposes a low dimensional approach to combine these data sources with weather data in order to forecast the daily demand for Bike Sharing Systems (BSS). The core of this approach lies in the proposed clustering technique, which reduces the dimension of the problem and, differently from other machine learning techniques, requires limited assumptions on the model or its parameters.The proposed clustering technique synthesizes mobility data quantitatively (number of trips) and spatially (mean trip origin and destination). This allows identifying recursive mobility patterns that - when combined with weather data - provide accurate predictions of the demand.The method is tested with real-world data from New York City. We synthesize more that four millions trips into vectors of movement, which are then combined with weather data to forecast the daily demand at a city-level. Results show that, already with a one-parameters model, the proposed approach provides accurate predictions.1","PeriodicalId":165866,"journal":{"name":"International Conference on Models and Technologies for Intelligent Transportation Systems","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122892713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}