[Paper Review] Understanding urban congestion with biking traffic and routing detour ratio

Xinze Qiu, Tianli Gao+4 more|arXiv (Cornell University)|May 17, 2022

Urban Transport and Accessibility5 citations

TL;DR

This study reveals a sublinear scaling relationship between urban vehicle congestion and a biking-traffic-weighted detour ratio—highlighting a strong interplay between cycling activity, road network topology, and traffic congestion. The metric enables detection of inefficient cycling routes and improves congestion prediction in data-scarce urban environments.

ABSTRACT

Bike-sharing systems have been regarded as a critical component of solutions towards the transition to greener and more sustainable transportation, with the benefits of reducing carbon emissions, improving public health, and mitigating congestion by replacing short-distance motorized trips. Due to better accessibility and usage flexibility, newly emergent dockless sharing bikes have become quite popular and are reviving the fashion of cycling in cities. Urban congestion is simultaneously influenced by heterogeneous saptio-temporal travel demands, topology and spatial characteristics of road networks, and the interplay between travel modes. In this paper, by considering aforementioned factors, we discover a robust sublinear scaling relation between the level of congestion for vehicles and the detour ratio weighted by biking traffic, which is intriguing given the fact that congestion and detour ratio is linearly independent. Such a scaling relation implies a strong interplay between vehicle traffic and cycling activities and can be applied in predictions for congestion or aggregated to more sophisticated traffic models. In addition, biking-traffic-weighted detour ratio can be applied to detect inefficient routes, which would help alleviate urban congestion, make better urban planning, and improve transportation efficiency and equity in cities.

Motivation & Objective

• To understand the interplay between vehicle congestion, cycling activity, and road network structure in urban environments.
• To quantify how detour ratios in biking trips influence overall urban congestion levels.
• To develop a scalable metric—biking-traffic-weighted detour ratio—for identifying inefficient cycling routes.
• To support congestion prediction and urban planning using limited traffic data by leveraging cycling behavior patterns.

Proposed method

• Utilizes massive real-world data from dockless bike-sharing systems and vehicle congestion metrics across three Chinese cities: Beijing, Shanghai, and Xiamen.
• Calculates detour ratio as the ratio of shortest path distance on road networks to Euclidean distance between origin and destination.
• Introduces a biking-traffic-weighted detour ratio (TDR) by aggregating detour ratios across trips, weighted by the number of bike trips on each route.
• Employs scaling analysis to identify a sublinear power-law relationship between congestion levels and TDR across cities.
• Visualizes inefficient routes using OSMnx, prioritizing segments with high TDR and high biking traffic for infrastructure improvement.
• Proposes a theoretical model linking congestion (t_busy / t_free) to traffic volume (V_ij / C_ij), detour ratio (DR_ij), and road capacity, using a power-law form: (1 + η(V_ij/C_ij))^β = (T_ij/T)^(α−1) × DR_ij^α.

Experimental results

Research questions

• RQ1 Is there a measurable scaling relationship between urban congestion and detour ratio weighted by biking traffic?
• RQ2 How do road network topology and heterogeneous spatio-temporal travel demands influence this relationship?
• RQ3 Can the biking-traffic-weighted detour ratio effectively identify inefficient cycling routes in urban areas?
• RQ4 To what extent does cycling activity modulate vehicle congestion, and vice versa, in the absence of dedicated cycling infrastructure?
• RQ5 How can this metric be used to improve congestion prediction and urban transportation planning with limited data?

Key findings

• A robust sublinear scaling relation is found between vehicle congestion and biking-traffic-weighted detour ratio across Beijing, Shanghai, and Xiamen, despite the linear independence of congestion and detour ratio.
• Detour ratio distributions for biking trips are consistent across cities and negatively correlate with Euclidean distance, indicating predictable routing behavior.
• Routes with high biking-traffic-weighted detour ratios are predominantly located at city fringes, around large roadblocks, or across natural barriers like rivers and mountains.
• Inefficient routes are often caused by urban design features such as wide roads, gated communities, or topographic barriers that force long detours.
• The metric successfully identifies priority corridors for infrastructure improvement, such as dedicated bike lanes through enclosed urban zones.
• The theoretical model (T_ij/T)^(α−1) × DR_ij^α ≈ (1 + η(V_ij/C_ij))^β demonstrates that congestion is influenced by both traffic volume and detour characteristics, enabling data-efficient congestion modeling.