Anticipating Spatial-Temporal Distribution of Regional Highway Traffic with Online Navigation Route Recommendation

Detailed anticipation of potential highway congestion is becoming more necessary, as increasing regional road traffic puts pressure on both highways and towns its passes through; tidal traffic during vacations and unsatisfactory town planning make the situation even worse. Remote sensing and on-site sensors can dynamically detect upcoming congestion, but they lack global and long-term perspectives. This paper proposes a demand-network approach that is based on online route recommendations to exploit its accuracy, coverage and timeliness. Specifically, a presumed optimal route is acquired for each prefecture pair by accessing an online navigation platform with its Application Programming Interface; time attributes are given to down-sampled route points to allocate traffic volume on that route to different hours; then different routes are weighted with the origin-destination traveler amount data from location-based services providers, resulting in fine-level prediction of the spatial-temporal distribution of traffic volume on highway network. Experiments with data in January 2020 show good consistency with empirical predictions of highway administrations, and they further reveal the importance of dealing with congestion hotspots outside big cities, for which we conclude that dynamic bypassing is a potential solution to be explored in further studies.

Automated summary

Detailed anticipation of highway congestion becomes more necessary with increasing regional road traffic, which puts pressure on highways and towns it passes through. This paper proposes a demand-network approach based on online route recommendations, which shows good consistency in predicting traffic volume distribution on the highway network. The study emphasizes the importance of dealing with congestion hotspots outside big cities and suggests dynamic bypassing as a potential solution.