Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone

Abstract

This research describes numerical methods to analyze the absolute transport demand of cyclists and to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle counts and map-matched GPS traces. The methods are demonstrated with data from the city of Bologna, Italy: approximately 27,500 GPS traces from cyclists were recorded over a period of one month on a volunteer basis using a smartphone application. One method estimates absolute, city-wide bicycle flows by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts at the main bikeways of the city. As there is a fairly high correlation between the two sources of flow data, the absolute bike-flows of the entire network have been correctly estimated. Another method describes a novel, total deviation metric per link which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route. The deviations are accepted by cyclists either to avoid unpleasant road attributes along the shortest route or to experience more favorable road attributes along the chosen route. The total deviation metric indicates to the planner which road links are contributing most to the total deviation of all cyclists. In this way, repellant and attractive road attributes for cyclists can be identified. This is why the total deviation metric is of practical help to prioritize bike infrastructure construction on individual road network links. Finally, the map-matched traces allow the calibration of a discrete choice model between two route alternatives, considering distance, share of exclusive bikeway, and share of low-priority roads.

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

“…They identify individual travels in urban areas and simulate commuting behaviours of residents using an agent-based model. Rupi, Poliziani [60] describe the use of numerical methods to match the network demand and supply of bicycles. This is a useful study in the improvement of the city infrastructure using spatial data sets.…”

Visual Eye Gaze While Cycling: Analyzing Eye Tracking at Signalized Intersections in Urban Conditions

“…Most travelers in most cities are accustomed to navigating in the urban environment within the confines of a car. Providing safer environments to encourage cycling more is a core challenge for city leaders [1][2][3][4], and in this landscape it is necessary to understand how elements of a city's infrastructure provide for or impede cycling comfort. More specifically, are there any specific factors that moderate how safe cycling is perceived by users?…”

Supply and Demand Analysis of a Free Floating Bike Sharing System

“…The climate is convenient for cycling all year, with an annual average temperature slightly below 15 °C and low rainfall (about 700 mm rain/ year and 74 days of rain per year). Bicycle flows have constantly grown in recent years in Bologna, along with an increasing bikeway supply [3]. Currently, there are 129 km bikeways of different types in the city (see Figure 1): 1) reserved bike lanes; 2) mixed access bike lanes with either pedestrian or buses; 3) bike lanes placed side by side to either footpaths or road; 4) dirt bike lanes; 5) recommended roads in the city center, only where there are no bike lanes; 6) paved bike-lanes inside city parks.…”

“…Congestion problems in urban centers, together with the issue of environmental sustainability, has led the European Union to develop regulations aimed at development of efficient and sustainable urban mobility system. Several studies show that cycling mobility has significantly increased in recent years [1][2][3][4], thus taking a pivotal role in the transition to an economically feasible, socially acceptable and environmentally friendly urban mobility. In this context, bike-sharing services represent organic and efficient systems, which offer both an alternative and an integration with more classic modes of transport.…”