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Visualization Tool to Analyze Data-Driven Bus Lane Allocation Guidelines

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Visualization Tool to Analyze Data-Driven Bus Lane Allocation Guidelines Ben-Gurion University of the Negev Faculty of Engineering Science Department of Industrial Engineering and Management Bussleneck – Visualization Tool to Analyze Data-Driven Bus Lane Allocation Guidelines Thesis Submitted in Partial Fulfillment of the Requirements for M.Sc. Degree Submitted By: Shaked Kaufman-Ofek Supervisors: Prof. Noam Tractinsky, Department of Software and Information Systems Engineering Dr. Eran Ben-Elia, Department of Geography and Environmental Development E-mail: [email protected] 15 December 2020 22 Ben-Gurion University of the Negev Faculty of Engineering Science Department of Industrial Engineering and Management Bussleneck – Visualization Tool to Analyze Data-Driven Bus Lane Allocation Guidelines Thesis Submitted in Partial Fulfillment of the Requirements for M.Sc. Degree Submitted By: Shaked Kaufman-Ofek Supervisors: Prof. Noam Tractinsky, Department of Software and Information Systems Engineering Dr. Eran Ben-Elia, Department of Geography and Environmental Development Author: ……………………………….……… Date: …………15/12/2020……………… Supervisor: …………………………………… Date: …………15/12/2020……………… Supervisor: …………………………………… Date: …………15/12/2020……………… Chairman of Graduate Studies Committee: …………………_ Date: …15/12/2020…… 15 December 2020 ii Abstract Public Transit (PT) plays a vital part in a sustainable mobility network. In addition to walking, cycling and micro-mobility, PT provides relatively cheap and highly efficient way to move masses of people within the city and between cities. In dense areas, heavy and light trains provide the backbone of the PT system to move masses of people, while buses provide greater coverage and more flexibility in both planning and operation. However, in some places, buses are the backbone of the system. In Israel, for example, buses deliver 85% of PT trips and the rest is delivered by other means (ISC 2019). In order to provide a reliable service, causes of delays should be removed. Buses can be delayed due to traffic, congestion, late departure, inadequate driving style, cash payments and more. To increase ridership and improve the travel experience, these should be mitigated. One of the major reasons for delays is congestion and general traffic that cause buses to travel at low speeds. Different means exist to mitigate this issue: bus lanes, bus preemption in traffic-lights, bus islands and more. The publication of guidelines for planning and operating public transportation bus services in Israel (Ashak and Shliselberg 2016) was a milestone in PT service planning and operation in the country. For the first time, an official government-issued document addressed data-driven bus lane allocation. The guidelines consider bus volumes, passenger volumes and bus speeds to justify bus priority. One may produce static maps or lists that describe where bus lanes should be allocated, but we aimed to offer a fundamental application that allows analyzing the underlying metrics and overviewing analysis outcomes in a comprehensive manner. Our work suggests Bussleneck, a data visualization application that implements those guidelines on two real-world use cases of the cities of Be’er Sheva and Tel Aviv–Yafo in Israel. We address data sources collection (bus schedules, aggregated real-time data records and on-board passenger counts), data processing into relevant metrics and different aspects of data visualization design. The application was evaluated by a group of experts who represents the intended audience of PT planners and PT related data analysts. The application will be available for public use, including source code and processed metrics. The application displays metrics based on data records collected during March 2019. We intend to do our best efforts to publish the application using up-to-date data sources. In this work we also developed a methodology to build a network of PT segments (road segments that carry PT vehicles [buses] delimited by either a bus stop or an intersection) from GTFS and OSM data. We also systematically surveyed different papers that utilize data visualization tools to analyze similar data sources. Finally, we provide recommendations to authorities regarding the use of OSM and suggest improvements to data sources that can contribute to higher precision of the analysis process. • A paper titled ‘Visualization Tool to Analyze Data-Driven Bus Lane Allocation Guidelines’ was presented at the Transit Data 2020 symposium. • The code for processing the data can be accessed on this GitHub repository, and the code for the application can be found on this repository. Bussleneck application itself can be accessed and used by the public. keywords: PT, Public Transit, Public Transportation, bus lane, SIRI, AVL, GTFS, APC, Data Visualization, Buses, Data-driven Planning iii Acknowledgments I would like to express my gratitude to my academic advisors, Prof. Noam Tractinsky and Dr. Eran Ben-Elia for their helpful advice and guidance, financial support, in-depth proofing and continuous guidance throughout the research. Though not an official academic advisor, I wish to thank Dr. Peter Bak for being my mentor and consultant for academic, technical and practical aspects of designing and developing data visualization tools. My research would have been more cumbersome without the help of Dror Bogin, whose research work and coding are the basis for my data processing methodologies. Similar gratitude goes to the authors and publishers of Open Street Map, QGIS software suite, React JS and D3 JS code packages and the following Python packages: Pandas, GeoPandas, GTFS Kit, SciPy and Shapely who made it possible to develop this application in timely manner. Incorporating passenger counts data records would not be possible without the help of The Ministry of Transport and Road Safety and the National Public Transport Authority personnel. I thank them for the cooperation and willingness to support my research by providing data records from different periods and high volumes to support this research. Special thank you goes to the Facebook ‘Transit Data Israel – API, R&D and data analysis’ group members, and especially Netta Beninson and Dan Bareket who provided endless consultation and suggestion on PT data processing in the Israeli context. Over three years of research, I worked with groups of B.Sc. students from the Department of Software and Information Systems Engineering. Though not included in this final work, their project works were an inspiration and a basis for the final products of this research. I wish to thank them for taking part in my wild ideas and high demands. More than everyone, my deepest gratitude goes to my partner Yoni who stood by me, endorsed me and encouraged me to pursue my research goals and make this work available for the public, and not just stay in the academic realm. Finally, this research was sponsored by the following scholarships which I personally thank: • The Ben Gurion University’s Vice Rector grant program for inter-disciplinary Masters studies • BGU Interdisciplinary Research with Faculty of Humanities and Soc. Sciences grant program • The Israeli Smart Transportation Research Center for funding the costs of attending and presenting at the Transit Data 2020 symposium. iv Table of Contents Abstract .............................................................................................................. iii Acknowledgments ............................................................................................... iv List of Tables ..................................................................................................... viii List of Figures ...................................................................................................... ix List of Equations .................................................................................................. xi 1. Introduction ..................................................................................................... 2 2. Background ..................................................................................................... 3 2.3.1. Bus Service Profiles ........................................................................................................... 5 2.4.1. Guidelines for Planning and Operating Public Transit Bus Services ................................. 6 2.4.2. Bus Lane Allocation Guidelines ........................................................................................ 7 2.5.1. GTFS – General Transit Feed Specification ....................................................................... 9 2.5.2. AVL – Automatic Vehicle Location ................................................................................. 10 2.5.3. APC – Automatic Passenger Counter ............................................................................. 10 2.6.1. The “What­Why­How” Model ......................................................................................... 12 3. Related Work ................................................................................................. 15 3.1.1. Publications Addressing Relevant Metrics ..................................................................... 15 3.1.2. Publications Discussing GTFS, AVL & APC Analysis In­depth .......................................... 19 4. Context .......................................................................................................... 29 v 4.3.1. Open Street Map Data...................................................................................................
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