Preempting Fire Engines at Traffic Signals in Brunswick, Germany, Using ITS-G5

Author(s):  
Sebastian Naumann ◽  
Joachim Schade
Keyword(s):  
Author(s):  
Sharmin-E-Shams Chowdhury ◽  
Aleksandar Stevanovic ◽  
Nikola Mitrovic

Pedestrian walk timings at most U.S. traffic signals are run in concurrence with relevant signal phases for vehicular traffic. This usually means that signal operations coordinated for the major street can be interrupted by a pedestrian call. Such an interruption may in practice last for a few minutes, thus causing increased delays and stops for major traffic flows. An alternative to this design is to increase the cycle length and embed pedestrian timings within the ring-barrier structure of the prevailing coordination plan. Both approaches have advantages and disadvantages. A fresh approach offered by this study is a comprehensive experimental design and holistic performance evaluation perspectives. The study examines the two abovementioned treatments of pedestrian timings for a small corridor of five intersections in Utah. The experiments have been done in a high-fidelity microsimulation environment with the Software-in-the-Loop version of the field controller (Econolite ASC/3). Findings show that either approach works well for very low traffic demands. When the traffic demand increases findings cannot be generalized as they differ for major coordinated movements versus overall network performance. While major-street traffic prefers no interruption of the coordinated operations, the overall network performance is better in the other case. This can be explained by the fact that avoiding interruptions is usually achieved at the expense of longer cycle length, which increases delay for everyone in the network.


Author(s):  
Rachel Aldred ◽  
Georgios Kapousizis ◽  
Anna Goodman

Objective: This paper examines infrastructural and route environment correlates of cycling injury risk in Britain for commuters riding in the morning peak. Methods: The study uses a case-crossover design which controls for exposure. Control sites from modelled cyclist routes (matched on intersection status) were compared with sites where cyclists were injured. Conditional logistic regression for matched case–control groups was used to compare characteristics of control and injury sites. Results: High streets (defined by clustering of retail premises) raised injury odds by 32%. Main (Class A or primary) roads were riskier than other road types, with injury odds twice that for residential roads. Wider roads, and those with lower gradients increased injury odds. Guard railing raised injury odds by 18%, and petrol stations or car parks by 43%. Bus lanes raised injury odds by 84%. As in other studies, there was a ‘safety in numbers’ effect from more cyclists. Contrary to other analysis, including two recent studies in London, we did not find a protective effect from cycle infrastructure and the presence of painted cycle lanes raised injury odds by 54%. At intersections, both standard and mini roundabouts were associated with injury odds several times higher than other intersections. Presence of traffic signals, with or without an Advanced Stop Line (‘bike box’), had no impact on injury odds. For a cyclist on a main road, intersections with minor roads were riskier than intersections with other main roads. Conclusions: Typical cycling environments in Britain put cyclists at risk, and infrastructure must be improved, particularly on busy main roads, high streets, and bus routes.


Author(s):  
Feifei Xin ◽  
Xiaobo Wang ◽  
Chongjing Sun

In recent years, conflicts between crossing pedestrians and right-turning vehicles have become more severe at intersections in China, where right-turning vehicles are usually not controlled by traffic signals. This study proposes a quantitative method for evaluating the conflict risk between pedestrians and right-turning vehicles at intersections based on micro-level behavioral data obtained from video detection. A typical intersection in Shanghai was selected as the study site. In total, 670 min of video were recorded during the peak hours from 7:30 a.m. to 9:30 p.m on one day. After processing the video information, vehicle and pedestrian tracking data were obtained, including the velocity, acceleration, deceleration, time, and location coordinates. Based on these data, several conflict indicators were proposed and these indicators were extracted automatically using MATLAB to identify pedestrian–right-turning vehicle conflicts and to determine the severity of the conflicts identified. This process identified 93 examples of such conflicts. The conflict risks were quantitatively classified using the K-means fuzzy clustering method and all of the conflicts were assigned to five grades. The characteristics of the conflict distribution and the severity of different types of conflict were also analyzed, which showed that conflicts on different areas on the crosswalk differed in their severity. Based on the conclusions, practical traffic management and control measures are proposed to reduce the risk on pedestrian crossings.


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