Background

Traffic congestion is one of the main focuses of traffic management. It is a state when traffic demand exceeds roadway capacity. The characteristic of traffic congestion occurring within urban road networks can be quite different from those taking place on freeways because of traffic signals, intersections and the complexity of road networks.

Traffic congestion can be further divided into recurrent one which usually occurs during peak hours and non-recurrent one resulting from a variety of incidents, such as traffic accidents, road construction as well as large activities.

Researchers are interested in several related topics, including the formation of traffic congestion, the estimation of negative effects caused by traffic congestion, the bottlenecks in the road network and the strategies to prevent as well as ease congestion. To answer the questions mentioned above, congestion incidents need to be identified from traffic data first. How to detect traffic congestion through a systematic approach of collecting and analyzing traffic data has been the key issue for traffic management. In previous studies, traffic data are often extracted from loop detectors. However, there are some obvious shortcomings, such as the difficulties in facility maintenance, high malfunctioning and misdetection rate. Hence, other types facilities for detection, for example, electronic toll collection (ETC) sensors, monitors and microwave vehicle detectors (VD) are installed. ETC system has been operating on the freeways in Taiwan since 2014. Besides improving the service level of the freeway system, it also contributes to the collection of large amount of traffic data. These data can be used for traffic

management and opened to both academia and individuals for extended applications. In Taipei City, vehicle detectors are widely installed within the urban road network, and high-resolution traffic data are collected. They provide abundant traffic data including point travel speed, traffic volume and occupancy. The daily VD data are provided without charge on the governmental open data platform, Data.Taipei website. Through the investigation of these data, the characteristics of traffic flows can be observed and a baseline traffic condition can be determined. By comparing the traffic data of a set of target VDs within a Region Of Interest (ROI) during a certain time interval with the baseline, congestion incidents can be detected. Traffic congestion may be manifested as a chain reaction, forming a shockwave across a certain scope of a roadway network (Li, She, Luo, & Yu, 2013). Some studies on traffic congestion forecasting have been conducted by employing pheromone communication models (Kurihara, Tamaki, Numao, Yano, Kagawa, & Morita, 2009), density wave models (Nagatani, 2002) and so on. To understand how a congestion incident may propagate throughout a network and dissipate based on the exploration of real data can be the research direction to further enhance urban traffic management.

In order to provide pedestrians and cyclists a safer environment, Taipei City government has been implementing the bike lane network plan since 2014. Considering the departure efficiency, that is, the time needed to eliminate the queue at traffic signals, three north-south arterials and three east-west arterials are selected. Each of them has a width of at least 40 meters and metro routes passes through four of them. The planned network is shown in Figure 1.1. For those with wider sidewalks, for example, Jen-Ai road and Zhong-Shan N. road, marking lines for bike lanes are painted on the original

are drawn. The layout of a widened sidewalk with a bike lane is shown in Figure 1.2.

Residents had been reporting the congestion and inconvenience during the bike lane construction on Fu-Xing S. Road and Xin-Sheng S. Road from March to September in 2016. According to the travel speed collected from vehicle detectors, during the construction, travel speed slightly decreased by 6.49% to 7.91% and the service level had been degraded (Taipei City Traffic Engineering Office, 2016). However, the service level had almost recovered after the construction work was completed. Hence, whether there are some differences in terms of the traffic flow characteristics and congestion propagation pattern between arterials under construction and the others is worth investigating. Moreover, more detailed understanding of relationships among neighboring road segments may also provide traffic management agencies and individuals valuable information for evaluating the influences of construction decisions, determining traffic management strategies and providing navigation. Hence, high-resolution VD data during the construction in an ROI covering the arterials under construction can be extracted for further analysis. Characteristics of the congestion propagation pattern including the conditional probability that a congestion may occur given the occurrence of another congestion, the potential relationship between adjacent road segments and how traffic congestion contribute to different road segments can be observed.

Figure 1.1 Planned Bike Lane Network in Downtown Taipei

Figure 1.2 Layout of Widened Sidewalk on Fu-Xing S. Road

In this study, we seek to obtain better understanding of the pattern of how traffic congestion propagates and influences a roadway network. The traffic control center of Taipei City has provided a system for real time traffic status inquiry by plotting the road performance information on a Google Map as partly shown in Figure 1.3. Straightforward information can be extracted based on the collected traffic data (Chen, Guo & Wang, 2015), while the cascading traffic pattern may further suggest driver behavior of diverting to circumvent congested road segments. Hence, the main purpose of this study is to go deeper to investigate the effects of congestion afterwards. To monitor where and when traffic congestion occurs, we take point vehicular speed as the primary consideration.

Based on the traffic data collected from vehicle detectors (VDs), we cluster these data by capturing the spatiotemporal variation of vehicular speed over the network so as to identify congestion incidents. Based on the congestion incidents identified, affected road segments can also be further determined.

Figure 1.3 Real Time Traffic Status of Taipei City

Ultimately, this research seeks to investigate the propagation of congestion incidents within an urban road network. By visualizing the bottle necks and shockwave after a congestion occurs, we provide some research insight so that a precautionary traffic management strategy may be taken.