Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data

Abstract

In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping‐to‐cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping‐to‐cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space‐space and time‐space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.

Exploring the Intermodal Relationship between Taxi and Subway in Beijing, China

“…The GPS data generated by more than 46,000 taxis [34,35] during January 12th-18th, 2015, were attained [33], which contained more than 2.9 million taxi trips. The interval of the GPS data is at around 1 minute.…”

Spreading Patterns of Malicious Information on Single‐Lane Platooned Traffic in a Connected Environment

“…Accurately, modeling urban traffic is a challenging task (Karim and Adeli, ; Levin et al., ; Wang et al., ; He et al. ; Ricardo et al., ). Over the years, two types of traffic flow models have been developed.…”

“…We start the modeling from the physical layer, which aims to describe the dynamics of urban traffic. Accurately, modeling urban traffic is a challenging task (Karim and Adeli, 2003;Levin et al, 2016;Wang et al, 2014;He et al 2017;Ricardo et al, 2017). Over the years, two types of traffic flow models have been developed.…”

Lossless Compression of All Vehicle Trajectories in a Common Roadway Segment

“…Some examples of studies whose data needs could be supported by a large trajectory repository include He et al. (), Duret et al. (), and Geroliminis and Ramezani ().…”

“…One can imagine many benefits of archiving as much of this trajectory information as possible, perhaps for perfecting various algorithms, but also for long-term study of traffic flow and characteristics, particularly as they change in response to these innovations. Some examples of studies whose data needs could be supported by a large trajectory repository include He et al (2017), Duret et al (2010), and Geroliminis and Ramezani (2014). The quantity of data produced by such activities could be enormous.…”