Characterizing Bus Travel Time using Advanced Data Visualization Techniques
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ORIGINAL ARTICLE
Characterizing Bus Travel Time using Advanced Data Visualization Techniques Rony Gracious1 · B. Anil Kumar2 · Lelitha Vanajakshi1 Received: 17 July 2019 / Accepted: 23 September 2020 © Springer Nature Switzerland AG 2020
Abstract With the introduction of various automated sensors, traffic data collection has become easier and huge amount of data are getting accumulated over time. One of the interesting challenges in the field of intelligent transportation systems is to effectively utilize such large-scale database. Making meaningful inferences out of this data by conducting in-depth analyses to identify different patterns/trends followed by the traffic variables can lead to the development of more efficient end-applications. The current study analyzes travel time data obtained from buses fitted with global positioning system devices to understand the temporal and spatial variations in travel time in the city of Chennai. For this, data visualization tools such as tree maps and heat maps were used. From temporal analysis, it was observed that travel times are increasing over the years and it was also observed that there is a discernible pattern in travel between weekdays and weekend. From spatial analysis, it was found that there exists a segment specific characteristic of travel time and certain segments experiencing higher travel times in urban areas particularly at intersections. The findings from the study were further used in demonstrating a possible user application, bus travel time prediction system, based on the identified patterns. Performance analysis showed a combination of inputs from same month last year, day of the week, and traffic conditions performing better for the considered dataset. Keywords Travel time patterns · Tree maps · Heat map · Data visualization · Temporal variation · Spatial variation
Introduction One major contribution of Information Technology (IT) to traffic data collection is the ability to locate moving vehicles using global positioning system (GPS) devices. Many transportation agencies and research organizations have been using GPS-based probe vehicles and other intelligent transportation systems (ITS) techniques for data collection over the past couple of decades. This is especially true with public transport buses. In India, most of the major cities have public transit buses fitted with GPS devices and are providing real * B. Anil Kumar [email protected] Rony Gracious [email protected] Lelitha Vanajakshi [email protected] 1
Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
Department of Civil and Environmental Engineering, Indian Institute of Technology Madras, Chennai, India
2
time vehicle location data at fixed time intervals (usually at 5–10 s). The stored GPS data can provide information such as speed, travel time, and dwell time that is experienced by each trip. This generates a huge amount of data in both real-time and historic contexts. A substantial challenge is to develop useful measures that tr
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