[H-TT17] Geographic Information System and Cartography

[HTT17-03] Extracting Traffic Corridors and Analyzing Relationship between Inter-District Travel Efficiency and Traffic Corridors Using People Flow Data

*Masao Takeuchi¹, Sunyong Eom², Tsutomu Suzuki¹ (1.University of Tsukuba, 2.Hanyang University)

Keywords:Travel Efficiency, Traffic Corridors, People Flow

Understanding the relationship between urban district characteristics and their connecting traffic corridors is crucial for effective urban planning and mobility management. This study aims to classify districts based on three key metrics - traffic volume, travel efficiency, and destination diversity - and examine their relationship with traffic corridors between adjacent districts using GPS data. The data used in this study are Floating population data of AGOOP Corp (hereinafter referred to as GPS data) in April 2019. We extracted stay points, which are defined as staying within a 50m radius for 20 minutes or more. A trip is defined as a straight line between stay points and trips exceeding 2 kilometers were employed. The analysis employed three primary metrics: (1) trip end density relative to average multiplier for traffic volume, (2) travel efficiency expressed as the 5th percentile (h/km) of time required per unit distance, and (3) destination diversity calculated using the Shannon-Weiner Species Diversity Index for inter-district trips. The amount of traffic in district i is expressed as the trip end density versus the average multiplier r_i^dens. Travel efficiency is expressed as the 5th percentile e_i(h/km) of the time required per unit distance e for each trip. The diversity of destinations is represented by the Shannon-Weiner Species Diversity Index H_i' for each district of trips to and from the destination. Based on the three metrics of all trips, we categorize districts using k-means method and the result was shown in Table 1. Districts were categorized into eight classes (C1-C8) according to the trip end density, where C1 represents the lowest density and C8 represents the highest density. Table 1 shows that 67.6% (3,629 districts) are lower-density than average, and 32.4% (1,738 districts) are high-density, short-duration, and high-diversity. Figure 2 shows that districts with density exceeding twice the average, coupled with high travel efficiency and destination diversity, were found to radiate from urban centers into suburban areas. Conversely, low-density, low-traffic-efficiency, and low-diversity types predominantly occupied mountainous regions and scattered suburban locations. To validate the relationship between district characteristics and transportation corridors, we classified links between adjacent districts into nine types based on traffic density ratio and speed ratio, comparing actual routes to theoretical shortest paths. These were categorized as high (H), medium (M), or low (L) for volume, and fast (F), medium (M), or slow (S) for speed, with HF designated as primary traffic corridors. Chi-square testing (p<0.001) confirmed significant correspondence between district types and corridor characteristics. The highest-density districts (C1) showed the greatest proportion of HF corridors (52.6%), while subsequent high-density districts (C2-C5) maintained 20-40% HF corridors. Lower-density districts (C7-C8) exhibited significantly fewer HF corridors but higher proportions of HS, LM, and LS connections, with C1 being 8.0 times more likely than C8 to feature traffic corridors. These findings demonstrate that extracted traffic corridors align with the distribution of districts exhibiting high inter-district accessibility, validating our methodology for identifying movement corridors within arbitrary spatial units. This comprehensive classification system provides valuable insights for urban planning and transportation network optimization, offering a data-driven approach to understanding the relationship between district characteristics and their connecting corridors.