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Using Archived Bus Automatic Vehicle Location Data to Identify Indications of Recurrent Congestion

Chen, Cheng
http://rave.ohiolink.edu/etdc/view?acc_num=osu1366320182

Abstract Details

2013, Doctor of Philosophy, Ohio State University, Civil Engineering.
This study focuses on using buses equipped with Automated Vehicle Location (AVL) systems as probes to detect recurrent congestion indications on urban streets. A previously developed method for finding indications of recurrent congestion for a given period of multiple days is used as the base method. An approach is proposed to determine appropriate values for parameters in this base method. The approach is expected to be general and could therefore be adopted for other applications. The set of appropriate values determined in an empirical application are used for subsequent empirical studies to investigate the reasonableness of methods proposed in this study. The approach is based on using two bus AVL data sets that should produce similar congestion indications. Parameter values that produce a high measure of correlation between the two data sets subject to other considerations are selected. A method is developed to determine periods of multiple days such that there is no change in recurrent traffic patterns during a period and that there is a change between different periods. The homogeneous days grouping method consists of two components: bottom-up clustering and re-grouping. The bottom-up clustering component iteratively combines consecutive groups of days that are similar in terms of the speed distributions obtained on days in the groups. Based on the groups of days determined by the bottom-up clustering component, the re-grouping component further combines non-consecutive groups if they are similar in terms of their speed distributions. The method is validated using three different approaches. The three validation approaches all support the promise of the proposed method. An investigation is conducted to determine possible influence of bus drivers’ reactions to schedules on indications of recurrent congestion. Two procedures are developed to handle two different cases of bus drivers’ reactions to schedules: driving at low speed and driving at high speed to adhere to a schedule. Each approach uses multiple criteria to determine time-of-day periods and locations of indications of the corresponding bus drivers’ reactions to schedules. The speeds observed within the time-of-day periods and locations where bus drivers’ reactions are indicated to exist are removed. Empirical results support the usefulness of fine-tuning the recurrent congestion detection method by removing false indications resulting from bus drivers’ reactions to schedules.
Rabi Mishalani, Ph.D. (Advisor)
Mark McCord, Ph.D. (Advisor)
Cathy Xia, Ph.D. (Committee Member)
133 p.
Civil Engineering; Transportation
recurrent congestion; bus probes; Automatic Vehicle Location; AVL; network sensing; traffic condition assessment

Recommended Citations

Citations

  • Chen, C. (2013). Using Archived Bus Automatic Vehicle Location Data to Identify Indications of Recurrent Congestion [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366320182

    APA Style (7th edition)

  • Chen, Cheng. Using Archived Bus Automatic Vehicle Location Data to Identify Indications of Recurrent Congestion. 2013. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1366320182.

    MLA Style (8th edition)

  • Chen, Cheng. "Using Archived Bus Automatic Vehicle Location Data to Identify Indications of Recurrent Congestion." Doctoral dissertation, Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366320182

    Chicago Manual of Style (17th edition)

osu1366320182
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© 2013, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.