CongestionGrid: A temporal visualization of road segment congestion level data

Abstract

Traffic congestions tend to have repeated patterns weekly, monthly, or yearly, due to repeated events such as work days, monthly salary day, and yearly holidays. The knowledge of temporal traffic patterns can help road users reduce their travel times by altering their departure time to avoid traffic jams at high traffic spots. This paper proposes an approach to help road users understand the temporal traffic patterns of a road segment with CongestionGrid, a platform for temporal visualization of congestion data. CongestionGrid highlights temporal patterns with respect to time-of-day and day-of-week by spatially arranging them in a grid as seen in Figure 1. The traffic states are represented by red, yellow, and green, which represent high traffic, normal traffic and low traffic, respectively. CongestionGrid allows users to explore the temporal traffic patterns by viewing the congestion data of a certain week or an aggregation of data over a period of time. We demonstrate CongestionGrid using congestion level data collected from 356 unique road segments in Bangkok over a period of 5.5 months.