Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data

The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values

Measuring Impacts of New Highways Capacity – A Discussion of Potential Survey Methods

The paper reviews survey methods that might be used to detect the various impacts of new highway capacity (changes in flow and network travel times; behavioural responses such as rerouting, change in departure times, change of mode, redistribution and change in trip frequency; and changes in land use). The review was conducted in the context of a study for TRRL which sought to establish the feasibility of measuring responses to new highway capacity. The paper considers, in turn, surveys of traffic flow, public transport usage and network travel times, methods of estimating origin-destination matrices and a variety of questionnaire and interview techniques which might be used to collect individual travel data (roadside interviews; stopline surveys; household interviews; trip-end interviews; self completion questionnaires; retrospective, prospective and stated preference questions; panel surveys and indepth interviews). There is also a brief discussion of methods to determine bight movements and land use effects. The paper should not be regarded as a source of detailed information about the various types of survey but rather as a review of their comparative strengths and weaknesses in the given context

Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories

In order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in a CRF model, which integrates the temporal-spatial context information flexibly. The driver route preference is also used to bolster the temporal-spatial context when a low GPS sampling rate impairs the resolving power of temporal-spatial context in CRF, allowing the map matching accuracy of uncertain GPS trajectories to get improved significantly. The experimental results show that our proposed algorithm is more accurate than existing methods, especially in the case of a low-sampling-rate

Cycling in the era of Covid-19: The effects of the pandemic and pop-up bike lanes on cycling practices

This paper addresses the effects of the pandemic and of Covid pop-up cycle lanes on cycling. A questionnaire survey was carried out in Geneva and Lausanne, Switzerland. The pandemic has strengthened the attractiveness of cycling both as a mode of transport and as a recreational activity, showing its resilience in a time of crisis. Covid cycle lanes implemented after the first lockdown have improved traffic conditions for cycling in terms of safety, directness and the overall experience. Beyond the recruitment of new cyclists, an effect of consolidating existing practices is observed through, for example, their extension to additional routes and motives. These pop-up cycle lanes have, however, been politically contested, and their reception varies in the population, depending mainly on mobility habits and political position. As both cities aim to increase their modal share of cycling, the challenge is to capitalize on the recent development of cycling, to provide suitable infrastructures, but also to find ways to deal with the controversies and to legitimate cycling as a fully-fledged means of transport

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Evaluation of roadway spatial-temporal travel speed estimation using mapped low-frequency AVL probe data

© 2020 Elsevier Ltd The rapid increase in the number of vehicles equipped with GPS devices has resulted in using automatic vehicle location (AVL) data as probes to identify traffic flow status as well as route travel speed on a very fine spatial-temporal scale. However, these traffic monitoring approaches heavily rely on the widely distributed probe vehicles in the network and the high frequency of these probe samples, which are rarely implemented in the real world. This study aims to analyze the applicability of providing accurate traffic flow information from four types of low-frequency AVL data. Each data source is applied for speed estimation to develop guidelines on GPS data requirements for travel speed estimation. First, the probe sample size of each data source on each target corridor is studied to reveal the road segments that have the potential for speed estimation, along with the GPS sampling frequency of each data source. Second, the impact of probe vehicle types, sample sizes, and GPS sampling frequency is analyzed. This study offers guidance in using GPS data to conduct speed estimation in different scenarios, which can be further implemented in a prototype software tool for estimating the real-time travel speed. This study has shown the applicability for speed estimation from four types of GPS data, where the transit bus GPS data provides the best mean speed estimation. The speed estimation results are compared with loop detector data on a test road segment to evaluate its accuracy. The comparison results show that given the current GPS data sample size and updating frequency, the transit bus GPS data can provide a reasonably accurate estimation of the traffic flow speed with a mean absolute speed difference of 6.96 km/h