Data Mining Practical Machine Learning Tools and Techniques

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Big Data Analytics in Intelligent Transportation Systems: A Survey

Perceived service quality in bus transit service: A structural equation approach

This paper presents applications of the Transit Capacity and Quality of Service Manual (TCQSM) methodologies to fixed-route transit systems in Mexico. The applications are focused on the quality of service part of the manual. The TCQSM provides a simple and clear way to analyze transit systems in other countries such as Mexico as there are no significant differences with transit systems in the US. However, several factors and thresholds such as walking distance, passenger load factors, transit supportive areas and the difference between transit and auto travel times should be further researched and adapted to local conditions since these values have a significant effect on the determination of the appropriate level-of-service (LOS). Furthermore, there are other performance measures such as on-time reliability, safety and security, and headway adherence that are not commonly reported by transit systems in Mexico and therefore, the TCQSM methodologies are not applicable. The paper describes the applications and the adjustments necessary to apply such methodologies and highlights its advantages and limitations for applications in Mexico.

Transit Capacity and Quality-of-Service Manual: Applications to Mexican Transit Systems

Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks

The growth of literature in the field of quality of service in the public transport PT sector shows increasing concern for a better understanding of the factors affecting service quality SQ in PT organizations and companies. A large variety of approaches to SQ have been developed in recent years owing to the complexity of the concept; the broad range of attributes required to evaluate SQ; and the imprecision, subjectivity, and heterogeneous nature of the data used to analyze it. Most of these approaches are based on customer satisfaction surveys. This paper seeks to summarize the evolution of research and current thinking as it relates to the different methodological approaches for SQ evaluation in the PT sector over the years and to provide a discussion of future directions.

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Quality of Service in Public Transport Based on Customer Satisfaction Surveys: A Review and Assessment of Methodological Approaches

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

A Decision Tree (DT) is a potential method for studying traffic accident severity. One of its main advantages is that Decision Rules (DRs) can be extracted from its structure. And these DRs can be used to identify safety problems and establish certain measures of performance. However, when only one DT is used, rule extraction is limited to the structure of that DT and some important relationships between variables cannot be extracted. This paper presents a more effective method for extracting rules from DTs. The method's effectiveness when applied to a particular traffic accident dataset is shown. Specifically, our study focuses on traffic accident data from rural roads in Granada (Spain) from 2003 to 2009 (both included). The results show that we can obtain more than 70 relevant rules from our data using the new method, whereas with only one DT we would have extracted only five relevant rules from the same dataset.

Juan de Oña

Papers

Perceived service quality in bus transit service: A structural equation approach

Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks

Quality of Service in Public Transport Based on Customer Satisfaction Surveys: A Review and Assessment of Methodological Approaches

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Analysis of traffic accident severity using Decision Rules via Decision Trees