Empirical Analysis of Traffic Bottleneck at Beijing Expressways
Empirical Analysis of Traffic Bottleneck at Beijing Expressways
Sheng Jin, Dianhai Wang, and Dongfang Ma
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Received 23 July 2013; Revised 16 September 2013; Accepted 16 September 2013
Academic Editor: Wuhong Wang
Copyright © 2013 Sheng Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The expressways in Beijing are confronted with more serious traffic congestions. Based on the survey data obtained from the typical sections at the expressways, the time dependent characteristics of traffic flow parameters were analyzed in detail and the data gap was found in this paper. The Fast Fourier Transform (FFT) method is proposed to transfer the data of traffic flow parameters for describing the fluctuation characteristics of traffic flow. Two methods of identification, the graph method and the control line method, were proposed as to the change time of traffic bottleneck forming and dissipating. The findings in this paper have already been applied in traffic management and ramp control at the expressways in Beijing.
1. Introduction
Nowadays, traffic congestions at the expressways have become more serious in Beijing, and the situation is deteriorating. One of the key reasons for traffic congestions is the merging conflicts of the entering and exiting traffic streams caused by the close distances from the upstream entrance ramp (or exit ramp) to the downstream one and the numerous entrance and exit ramps [1–3]. Traffic bottlenecks form easily in the conflict zones along the expressways, and the queue spilled over may result in congestion or even jam when traffic demand increases. Therefore, it is necessary to research on the traffic bottleneck for traffic management or ramp metering.
A prosperous literature can be observed on this topic. Bertini and Cassidy presented some traffic features at freeway bottlenecks. Observations from two-freeway bottlenecks in and near Toronto, Canada, indicated that average vehicles discharge rate from a queue could be 10% lower than the flow measured prior to the queue’s formation. The present findings came by virtually comparing transformed curves of cumulative vehicle arrival number versus time and cumulative occupancy versus time measured at neighboring loop detectors [4]. Muoz and Daganzo researched the bottleneck mechanism of a freeway divaricating and found that an off-ramp queue may hamper freeway flow much more than an on-ramp bottleneck does [5]. By analyzing bottleneck formation on freeways, Das and Levinson identified “active bottleneck” locations on freeways and sections where bottlenecks occurred because of disturbances caused by downstream bottlenecks propagating backwards in the form of shockwaves [6]. Ogut and Banks worked on the stability of traffic flow at freeway bottlenecks [7]. Hu and Schonfeld developed a traffic simulation and optimization model to analyze traffic flow in large networks with severe queuing and to transfer traffic volume at bottlenecks [8].
Most of the work about traffic bottleneck mainly focused on the freeways, but a few on urban expressways, especially on those metropolises as in Beijing. Beijing Municipal Institute of City Planning and Design simply compared the traffic flow characteristics of freeways with those in some developed countries. It should be noted that many transport policies are under discussion in Beijing for congestion mitigation, for example, implementing the traffic congestion pricing [9, 10] and prompting the public transport systems [11]. In this paper, in order to analyze the traffic bottleneck formation, the time dependent characteristics of traffic flow parameters are analyzed in detail and the data gap is found on the basis of the survey data of typical sections chosen at Beijing expressways. The graph method and the control line method are put forward in order to assess the critical time of traffic state transition during bottleneck formation and dissipation. The former determines the critical time by adjusting curves of cumulative arrival vehicle number versus time and cumulative occupancy versus time. The latter designs an index, relative time occupancy which can better describe the traffic state, to calibrate the top and bottom boundary of the data gap according to the quality management principle.
2. Traffic Flow Characteristics Analysis2.1. Time Dependent Characteristics
The field data (traffic volume, speed, and time occupancy) of a merge area and its upstream and downstream were obtained through the video survey between Sitong Bridge and Lianxiang Bridge in North Ring III at Beijing expressways from 7:00 a.m. to 10:00 a.m. (including morning peak hour) and from 16:00 p.m. to 19:00 p.m. (including evening peak hour) between June and J (...truncated)