Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

Abstract

Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures. The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories, but also of the inspection of the embedded geographical context. In this paper, we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters, which are then represented as polygons. For representing temporal variations of the created polygons, we enrich these with vehicle trajectories of other times of the day and additional road network information. In a case study, we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project. The first test results show strong correlations with periodical traffic events in Shanghai. Based on these results, we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.

Using Cartograms for Visualizing extended Floating Car Data (xFCD)

“…When enriching this output with corresponding spatio-temporal data using an external device, extended Floating Car Data is being produced (Ortenzi et al 2010). Previous research has investigated the detection of critical situations or bottlenecks within road networks (Keler et al 2017a), calculating travel times (Pfoser 2008, Rahmani et al 2015, extracting traffic flow patterns (Jahnke et al 2017, Keler et al 2017b) and investigating environmental effects like carbon dioxide concentration or particulate matter emissions (Gühnemann et al 2004, Röger et al 2018. In Germany, the enviroCar 2 project has been initiated by a number of research partners to collect floating car data via a crowd-sourced approach.…”

QuadGridSIM: A quadrilateral grid‐based method for high‐performance and robust trajectory similarity analysis

“…Each point contains a spatial location , where and represent its geographic location (i.e., longitude and latitude), and t is its location timestamp. Trajectory data have been extensively used in various fields because of their rich information in terms of time, space, and semantics, including intelligent transportation (Keler et al., 2017; Li et al., 2019), urban computing (Dodge et al., 2020; Guo et al., 2022), and social sensing (Li et al., 2016; Liu et al., 2015). Scholars have conducted studies on various trajectory data analysis methods, among which the trajectory similarity measure is one of the most fundamental algorithms for trajectory data mining (e.g., trajectory clustering, pattern mining, and trajectory classification) (Wang et al., 2021; Zheng, 2015).…”

Vehicle detection using panchromatic high-resolution satellite images as a support for urban planning. Case study of Prague’s centre

“…The rapidly growing number of vehicles causes various traffic problems (Tan et al 2020), which are still difficult to measure and predict (Stuparu et al 2020). Besides tremendous emissions caused by traffic in air, land, and water, there is a gigantic loss of time and money every day in the world due to vehicle traffic congestion (Keler et al 2017;Stuparu et al 2020;Tan et al 2020). Tracked movement of objects is nowadays widely available and used for various applications in our society (Keler et al 2017).…”

“…Besides tremendous emissions caused by traffic in air, land, and water, there is a gigantic loss of time and money every day in the world due to vehicle traffic congestion (Keler et al 2017;Stuparu et al 2020;Tan et al 2020). Tracked movement of objects is nowadays widely available and used for various applications in our society (Keler et al 2017). Such monitoring can improve the prediction of short-term and long-term traffic situations based on better understanding of traffic congestion propagation in time and space.…”