Transportmetrica最新文献

{"title":"Scenario-based multi-objective AVI reader location models under different travel demand patterns","authors":"A. Chen, S. Pravinvongvuth, P. Chootinan","doi":"10.1080/18128600902929617","DOIUrl":"https://doi.org/10.1080/18128600902929617","url":null,"abstract":"Recently, a multi-objective model for locating the automatic vehicle identification (AVI) readers in a transportation network was proposed by the authors along with its solution procedure. The model locates the AVI readers by simultaneously considering three objectives: the equipment and installation cost (e.g. number of AVI readers), the coverage of the AVI system (e.g. number of origin–destination (O–D) pairs), and the amount/quality of travel information obtained (e.g. number of AVI readings). However, only a single travel demand pattern for a certain time period, such as the evening peak hour, was considered in determining the AVI reader locations. Therefore, the recommended AVI system may not be able to guarantee the amount of travel information gathered during other time periods. This study extends our previously proposed model by developing three scenario-based models to accommodate different travel demand patterns observed during the whole day. A case study is provided to illustrate the applicability of the new models and the robustness of the AVI system designed by considering different travel demand patterns.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"6 1","pages":"53 - 78"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600902929617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59924742","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":"Data modelling in transport","authors":"A. Chen, Seungjae Lee, Dongjoo Park","doi":"10.1080/18128600902929575","DOIUrl":"https://doi.org/10.1080/18128600902929575","url":null,"abstract":"Quality data is a key to developing good transportation models. In this special issue on ‘Data Modelling in Transport,’ researchers tackle different issues in collecting, managing, and making use of different data types to enhance transportation modelling. In the first paper, Tam, Lam and Lo develop a discrete choice model with an attitudinal variable to better understand how passengers perceive service quality in ground access mode choices to the Hong Kong International Airport (HKIA). A structural equation model is adopted to construct a ‘satisfaction’ latent variable that describes the causal relationship between the satisfaction latent variable and the observable variables of the trip and of the passengers. This satisfaction latent variable is then incorporated into the discrete choice model as an additional explanatory variable to improve the significance of the choice model and to better explain the mode choice behaviour of departing air passengers. The model results indicate that inclusion of the satisfaction latent variable is not only important in improving the significance of the choice model, but also reveals that passengers do consider both observed service variables and unobserved attitudinal variable for their ground access to the HKIA. In the second paper, Chang et al. provide a practical dynamic algorithm to forecast multi-interval bus travel time. The algorithm is developed based on the Nearest Neighbour Non-Parametric Regression (NN-NPR) using historical and current data collected by the Automatic Vehicle Location (AVL) technology. To show proof of concept, the algorithm has been applied to an intra-city bus route in Seoul, Korea. Preliminary results show that the algorithm performs well in both prediction accuracy and execution time, which are important for real-time applications. The third paper by Lee et al. presents an interesting study of using the power prior to enhance road safety analysis. Specifically, the authors develop a full Bayesian approach with the power prior to estimate regression coefficients and effectiveness of road safety countermeasures. An estimation procedure based on the Metropolis–Hastings algorithm, one of the Markov Chain Monte Carlo methods, is suggested for Bayesian computation. A case study is conducted using two rural national roads in Korea. The results indicate that the full Bayesian approach performs favourably compared to the empirical Bayesian approach. Automatic vehicle identification (AVI) has been considered as one of the emerging technologies used to collect real-time traffic information in a transportation network. In the fourth paper, Chen et al. provide three scenario-based multiobjective models to determine the locations of AVI readers under different travel demand patterns. The objectives are to consider the equipment and installation cost (e.g. number of AVI readers), the coverage of the AVI system (e.g. number of origin–destination (O–D) pairs),","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"6 1","pages":"1 - 2"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600902929575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59925066","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":"Dynamic multi-interval bus travel time prediction using bus transit data","authors":"Hyun-ho Chang, Dongjoo Park, Seungjae Lee, Ho-sang Lee, S. Baek","doi":"10.1080/18128600902929591","DOIUrl":"https://doi.org/10.1080/18128600902929591","url":null,"abstract":"The objective of this research is to develop a dynamic model to forecast multi-interval path travel times between bus stops of origin and destination. The research also intends to test the proposed model using real-world data. This research was brought about by the shortcomings of the existing real-time based short-term-prediction models, which have been widely utilised for single interval predictions. The developed model is based on the Nearest Neighbour Non-Parametric Regression using historical and current data collected by the Automatic Vehicle Location technology. In a test with real-world bus data in Seoul, Korea, the proposed multi-interval-prediction model performed effectively in terms of both prediction accuracy and computing time.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"6 1","pages":"19 - 38"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600902929591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59924693","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":"Evaluation of discrete choice and neural network approaches for modelling driver compliance with traffic information","authors":"Hussein Dia1, Sakda Panwai","doi":"10.1080/18128600903200596","DOIUrl":"https://doi.org/10.1080/18128600903200596","url":null,"abstract":"This article evaluates dynamic driver behaviour models that can be used, in the context of intelligent transport systems (ITS), to predict drivers’ compliance with traffic information. The inputs to this type of models comprise drivers’ individual socio-economic characteristics and other variables that may influence their compliance behaviour. The output is a binary integer representing whether drivers comply with travel advice or not. Two approaches are available for formulating this category of classification problems: discrete choice models and artificial neural networks (ANNs). The literature on this topic clearly points to the limitations of the discrete choice approach which suffers from assumptions of perfect information about travel conditions, infinite information processing capabilities of drivers and inability to model the uncertainty in driver decision making or the vagueness in information received from ITS devices. ANNs, on the other hand, are able to deal with complex non-linear relationships, are fault tolerant in producing acceptable results under imperfect inputs and are suitable for modelling reactive behaviour which is often described using rules, linking a perceived situation with appropriate action. This study aims to evaluate the performance of these two categories of models based on a common data set of driver behaviour, collected from a field behavioural survey on a congested commuting corridor in Brisbane, Australia. This article proposes the combination of fuzzy logic and neural networks as a viable approach for overcoming the limitations of existing algorithms by modelling drivers as heterogeneous individuals. The results showed superior performance for a neuro-fuzzy model over binary choice models in terms of classifying or predicting the categories of drivers most likely to comply (or not comply) with traffic advice. The accuracy of the proposed model, in terms of classification rate, ranged between 95 and 97% compared to 50–73% for the discrete choice models.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"65 1","pages":"249 - 270"},"PeriodicalIF":0.0,"publicationDate":"2009-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600903200596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59924829","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 passing gap acceptance model for two-lane rural highways","authors":"Haneen Farah, S. Bekhor, A. Polus, T. Toledo","doi":"10.1080/18128600902721899","DOIUrl":"https://doi.org/10.1080/18128600902721899","url":null,"abstract":"Passing manoeuvres on rural two-lane highways significantly affect highway capacity, safety and level of service. This article presents an analysis of data on drivers’ passing decisions on two-lane rural highways that were collected with an interactive driving simulator. Measurements of the speeds and positions of all vehicles in several different scenarios were collected and processed to generate observations of gap acceptance behaviour. In addition, participants responded to a questionnaire which collected information on their socio-demographic and driving styles characteristics. These data were utilised to develop a model that explains the decision whether to pass or not, using variables that capture the impact of the road geometry, traffic conditions and drivers’ characteristics. It was found that while the traffic related variables had the most important effect on passing decision, factors related to the geometric design and the driver characteristics also had a significant effect on these decisions.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"5 1","pages":"159 - 172"},"PeriodicalIF":0.0,"publicationDate":"2009-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600902721899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59924770","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":"Modelling the operational effects of unconventional U-turns at a highway intersection","authors":"J. Shahi, Abdoul-Ahad Choupani","doi":"10.1080/18128600902795042","DOIUrl":"https://doi.org/10.1080/18128600902795042","url":null,"abstract":"Conventional solutions for congested urban arterials have been exhausted. Recently, some signalised intersections have been replaced with unconventional unsignalised U-turns along six-lane divided arterials. In order to evaluate the new intersection design, it was necessary to quantify impacts of U-turns on movements through modelling. Some models exist for this purpose. However, these models seemed unlikely to be applicable for the unconventional U-turns. Hence, their impacts on travel time and speed of movements were modelled during a study project. Comparisons of the new and previous models affirm that previous models cannot predict the travel time and other variables as well as new models. Two useful applications of these models regarding the feasibility studies and determining optimal location of the U-turns are also presented. This article also outlines that the vehicles, which need to access specific lanes, are the source of turbulence observed in weaving sections of U-turns. Therefore, a new definition of volume ratio is introduced.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"57 1","pages":"173 - 191"},"PeriodicalIF":0.0,"publicationDate":"2009-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600902795042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59924775","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":"Analysis of feedback to obtain steady-state solutions in four-step modelling","authors":"Xuesong Feng, Junyi Zhang, A. Fujiwara","doi":"10.1080/18128600902717459","DOIUrl":"https://doi.org/10.1080/18128600902717459","url":null,"abstract":"The four-step travel demand model with feedback has been widely applied in developed cities; in contrast, its applications in developing countries are still very limited. When applying the model with feedback, it is important to figure out what kinds of feedback solutions should be used to obtain the uniquely converged outputs in an efficient way. Therefore, in this study, using the person-trip data collected in Jabodetabek metropolitan area of Indonesia in 2002, a newly developed four-step feedback model has been applied to compare the performances of different feedback solutions. Two major methods, i.e. the Constant Weights (CW) method and the Method of Successive Averages (MSA), are dealt with, where the CW method includes the conventionally applied Direct Feedback (DF) method as a special case. An empirical analysis result reveals that the application of the new feedback model based on the DF method converges most efficiently. It is further found that when using the CW method, the weight on the new matrix should be at least not less than 0.50 to make the feedback procedure converge quickly. Moreover, it is numerically confirmed that the trip matrices from different feedback solutions for one certain feedback process will lead to a unique convergence; in addition, the successive travel impedance matrices from any certain one of the feedback solutions could also converge finally.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"5 1","pages":"215 - 227"},"PeriodicalIF":0.0,"publicationDate":"2009-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600902717459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59924759","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":"Traffic forecasting using least squares support vector machines","authors":"Yang Zhang, Yuncai Liu","doi":"10.1080/18128600902823216","DOIUrl":"https://doi.org/10.1080/18128600902823216","url":null,"abstract":"Accurate and timely forecasting of traffic parameters is crucial for effective management of intelligent transportation systems. Travel time index (TTI) is a fundamental measure in transportation. In this article, a non-parametric technique called least squares support vector machines (LS-SVMs) is proposed to forecast TTI. To the best of our knowledge, it is the first time to cooperate the rising computational intelligence technique with state space approach in traffic forecasting. Five other baseline predictors are selected for comparison purposes because of their proved effectiveness. Having good generalisation ability and guaranteeing global minima, LS-SVMs perform better than the others. Experimental results demonstrate that our predictor can significantly reduce mean absolute percentage errors and variance of absolute percentage errors, especially for predicting traffic data with weak regularity. Persuasive comparisons clearly show that it provides a large improvement in stability and robustness, which reveals that it is a promising approach in traffic forecasting and time series analysis.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"5 1","pages":"193 - 213"},"PeriodicalIF":0.0,"publicationDate":"2009-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600902823216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59924809","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":"Data fusion for updating information in modelling drivers’ choice behaviour","authors":"M. Dell'Orco, D. Teodorovic","doi":"10.1080/18128600802630232","DOIUrl":"https://doi.org/10.1080/18128600802630232","url":null,"abstract":"In this article, a method for fusing fuzzy data relevant both to drivers’ experience and provided information is presented. Expected travel time is then updated according to the results of fusion. The method takes into account the ‘compatibility’ of data originating from different sources, and provides information about acceptability of results. Influence of uncertainty on drivers’ compliance with provided information is examined in detail, according to uncertainty-based information theory.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"43 1","pages":"107 - 123"},"PeriodicalIF":0.0,"publicationDate":"2009-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600802630232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60523526","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":"On the existence and uniqueness of first best tolls in networks with multiple user classes and elastic demand","authors":"A. Clark, A. Sumalee, S. Shepherd, R. Connors","doi":"10.1080/18128600802673281","DOIUrl":"https://doi.org/10.1080/18128600802673281","url":null,"abstract":"System optimal (SO) or first best pricing is examined in networks with multiple user classes and elastic demand, where different user classes have a different average value of value of time (VOT). Different flows (and first best tolls) are obtained depending on whether the SO characterisation is in units of generalised time or money. The standard first best tolls for time unit system optimum are unsatisfactory, due to the fact that link tolls are differentiated across users. The standard first best tolls for the money unit system optimum may seem to be practicable, but the objective function of the money unit system optimum is nonconvex, leading to possible multiple optima (and non-unique first best tolls). Since these standard first best tolls are unsatisfactory, we look to finding common money tolls which drive user equilibrium flows to time unit SO flows. Such tolls are known to exist in the fixed demand case, but we prove that such tolls do not exist in the elastic demand case. Although common money tolls do not exist which drive the solution to the exact time system optimal flows, tolls do exist which can push the system close to time system optimum (TSO) flows.","PeriodicalId":49416,"journal":{"name":"Transportmetrica","volume":"5 1","pages":"141 - 157"},"PeriodicalIF":0.0,"publicationDate":"2009-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/18128600802673281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60523768","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}