Enid Zhang
Understanding Urban Traffic Congestion Patterns and Causes at Scenic Locations in China
Traffic congestion is defined as when the usage of a transport system approaches full capacity or exceeds its design capacity (Zhao and Hu, 2019). Concerning urban roadways, it means the number of vehicles exceeding the permissible limit of the road (Pi et al., 2019). Due to high population density, the growth of vehicles and their infrastructure, and the increasing prevalence of shared mobility and delivery services, traffic congestion has become a global phenomenon (Wang & Debbage, 2021). This gives rise to numerous issues. Regarding human health, congestion reduces air quality by enhancing traffic-related air pollutants such as NOx and CO (Zhang and Batterman, 2013), and it also jeopardises road safety by increasing fatality and injury accident rates (Green, Heywood and Navarro, 2016; Li, Graham and Majumdar, 2012). Through an economic lens, traffic congestion engenders substantial economic losses attributable to factors such as additional travel time, fuel wastage, and productivity loss (Fleming, 2019; Schrank et al., 2015). The aggregate economic cost attributable to traffic congestion in the United States amounted to approximately 87 billion US dollars in 2018. Similarly, traffic congestion in European Union countries results in annual economic losses nearing 100 billion euros, representing approximately 1% of the region's Gross Domestic Product (Pei et al., 2021). Other negative effects include an increase in energy consumption (Mage et al., 1996), declines in the quality of life (Arnott and Small, 1994), and deteriorating urban ecological environments (Barth and Boriboonsomsin, 2008).
Within the realm of traffic congestion challenges, traffic congestion during public holidays presents a particularly challenging concern. This issue is pronounced in China and other developing countries, where the rapid rise in income levels has spurred a swift expansion of the tourism industry (Bao et al., 2017). During the 2024 Spring Festival holiday, 474 million Chinese tourists embarked on domestic travels, in stark contrast to the 40 million recorded in 1999 (State Council of the People's Republic of China, 2024; Wang et al., 2015). The prevalence of severe congestion during public holidays has become a recurring headline. Public holidays should provide a moment for individuals to engage in recreational activities and unwind. However, the prevalence of traffic congestion necessitates a significant portion of their time consumed on the roads, resulting in a notable loss of social productivity and an overall decline in the quality of the vacation experience (Smallwood, Beckley and Moore, 2011).
Despite extensive research on traffic congestion, there is some academic controversy on the topic and the issue of urban traffic congestion during holidays has not seen significant improvement. The inconclusive findings and ineffective outcome are partly due to a lack of fundamental understanding of the problem (Zhou et al., 2021). Understanding the patterns and causes of urban congestion is a prerequisite for grasping its nature and formulating effective transport policies and management plans (Chow et al., 2013). However, prevailing research and measures frequently bypass the fundamental issues, favouring symptom-focused approaches such as congestion prediction, signal light enhancements, and road redesign. This highlights the need for a more direct and detailed empirical analysis of the fundamental patterns and causes. Additionally, holiday-related traffic issues have been relatively overlooked in academic discussions (Bao et al., 2017). Given the flexibility in time and space of holiday travel compared to weekday commuter traffic and weekend trips, which results in more varied and sporadic travel behaviours, it necessitates specialised research (Wang et al., 2015).
This dissertation, approaching from the perspective of the built environment, aims to understand the spatiotemporal patterns and underlying causes of traffic congestion during post-COVID public holidays. Using the May Day holiday period in 2023 at the Big Wild Goose Pagoda Scenic Area in Xi'an, China as a case study, the following research questions will be addressed: (1) What are the temporal patterns of traffic congestion; (2) What are the spatial patterns of traffic congestion; (3) What built environment-related factors contribute to traffic congestion; (4) How do these factors correspond to the spatiotemporal patterns.
This study makes the following contributions to previous research. First, this paper explores traffic congestion during holidays and links congestion patterns with causes, supplementing current studies that mainly focus on weekdays and separate the analysis of congestion patterns and causes. Second, this study has broadened our understanding of the causes of traffic congestion, confirming the role played by certain built environment factors that have been overlooked by traditional research disciplines. Furthermore, the study focuses on the latest data, which is more timely in the post-pandemic context compared to previous research. All these points contribute to informing urban planners and transport policymakers about measures aimed at mitigating traffic congestion effectively.
Within the realm of traffic congestion challenges, traffic congestion during public holidays presents a particularly challenging concern. This issue is pronounced in China and other developing countries, where the rapid rise in income levels has spurred a swift expansion of the tourism industry (Bao et al., 2017). During the 2024 Spring Festival holiday, 474 million Chinese tourists embarked on domestic travels, in stark contrast to the 40 million recorded in 1999 (State Council of the People's Republic of China, 2024; Wang et al., 2015). The prevalence of severe congestion during public holidays has become a recurring headline. Public holidays should provide a moment for individuals to engage in recreational activities and unwind. However, the prevalence of traffic congestion necessitates a significant portion of their time consumed on the roads, resulting in a notable loss of social productivity and an overall decline in the quality of the vacation experience (Smallwood, Beckley and Moore, 2011).
Despite extensive research on traffic congestion, there is some academic controversy on the topic and the issue of urban traffic congestion during holidays has not seen significant improvement. The inconclusive findings and ineffective outcome are partly due to a lack of fundamental understanding of the problem (Zhou et al., 2021). Understanding the patterns and causes of urban congestion is a prerequisite for grasping its nature and formulating effective transport policies and management plans (Chow et al., 2013). However, prevailing research and measures frequently bypass the fundamental issues, favouring symptom-focused approaches such as congestion prediction, signal light enhancements, and road redesign. This highlights the need for a more direct and detailed empirical analysis of the fundamental patterns and causes. Additionally, holiday-related traffic issues have been relatively overlooked in academic discussions (Bao et al., 2017). Given the flexibility in time and space of holiday travel compared to weekday commuter traffic and weekend trips, which results in more varied and sporadic travel behaviours, it necessitates specialised research (Wang et al., 2015).
This dissertation, approaching from the perspective of the built environment, aims to understand the spatiotemporal patterns and underlying causes of traffic congestion during post-COVID public holidays. Using the May Day holiday period in 2023 at the Big Wild Goose Pagoda Scenic Area in Xi'an, China as a case study, the following research questions will be addressed: (1) What are the temporal patterns of traffic congestion; (2) What are the spatial patterns of traffic congestion; (3) What built environment-related factors contribute to traffic congestion; (4) How do these factors correspond to the spatiotemporal patterns.
This study makes the following contributions to previous research. First, this paper explores traffic congestion during holidays and links congestion patterns with causes, supplementing current studies that mainly focus on weekdays and separate the analysis of congestion patterns and causes. Second, this study has broadened our understanding of the causes of traffic congestion, confirming the role played by certain built environment factors that have been overlooked by traditional research disciplines. Furthermore, the study focuses on the latest data, which is more timely in the post-pandemic context compared to previous research. All these points contribute to informing urban planners and transport policymakers about measures aimed at mitigating traffic congestion effectively.