Effects of traffic signal coordination on the safety performance of urban arterials

Urban traffic congestion and crashes have been considered by city planners as critical challenges to the economic development of the city. Traffic signal coordination, which connects a series of signals along an arterial by various coordination methodologies, has been proved as one of the most cost-effective means of reducing traffic congestion. In this regard, Metropolitan Planning Organizations (MPO) or Transportation Management Centers (TMC) have included signal timing coordination in their strategic plans. Nevertheless, concerns on the safety effects of traffic signal coordination have been continuously raised by both transportation agencies and the public. This is mainly because signal coordination may increase the travel speed along an arterial, which increases the risk and severity of traffic collisions. To date, there is neither solid evidence from the field to support the concern, nor theoretical-level models to analyze this issue. This research aims to investigate the effects of traffic signal coordination on the safety performance of urban arterials through microsimulation modeling of two traffic operational conditions: free signal operation and coordinated signals, respectively. Three urban arterials in Reno, Nevada were selected as the simulation testbed and were coded in the PTV VISSIM software. The simulated trajectory data were analyzed by the Surrogate Safety Assessment Model (SSAM) to estimate the number of traffic conflicts. Sensitivity analyses were conducted for various traffic demand levels. Results show that under unsaturated conditions, traffic signal coordination could reduce the number of conflicts in comparison with the free signal operation condition. However, under oversaturated conditions, no significant difference was found between coordinated and free signal operations. Findings from this research indicate that traffic signal coordination has the potential to reduce the risk of crashes on urban arterials under unsaturated conditions.

Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks

Accurate and prompt traffic data are necessary for the successful management of major events. Computer vision techniques, such as convolutional neural network (CNN) applied on video monitoring data, can provide a cost-efficient and timely alternative to traditional data collection and analysis methods. This paper presents a framework designed to take videos as input and output traffic volume counts and intersection turning patterns. This framework comprises a CNN model and an object tracking algorithm to detect and track vehicles in the camera’s pixel view first. Homographic projection then maps vehicle spatial-temporal information (including unique ID, location, and timestamp) onto an orthogonal real-scale map, from which the traffic counts and turns are computed. Several video data are manually labeled and compared with the framework output. The following results show a robust traffic volume count accuracy up to 96.91%. Moreover, this work investigates the performance influencing factors including lighting condition (over a 24-h-period), pixel size, and camera angle. Based on the analysis, it is suggested to place cameras such that detection pixel size is above 2343 and the view angle is below 22°, for more accurate counts. Next, previous and current traffic reports after Texas A&M home football games are compared with the framework output. Results suggest that the proposed framework is able to reproduce traffic volume change trends for different traffic directions. Lastly, this work also contributes a new intersection turning pattern, i.e., counts for each ingress-egress edge pair, with its optimization technique which result in an accuracy between 43% and 72%.

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models

Although studies have previously investigated the spatial factors of COVID-19, most of them were conducted at a low resolution and chose to limit their study areas to high-density urbanized regions. Hence, this study aims to investigate the economic-demographic disparities in COVID-19 infections and their spatial-temporal patterns in areas with different population densities in the United States. In particular, we examined the relationships between demographic and economic factors and COVID-19 density using ordinary least squares, geographically weighted regression analyses, and random forest based on zip code-level data of four regions in the United States. Our results indicated that the demographic and economic disparities are significant. Moreover, several areas with disadvantaged groups were found to be at high risk of COVID19 infection, and their infection risk changed at different pandemic periods. The findings of this study can contribute to the planning of public health services, such as the adoption of smarter and comprehensive policies for allocating economic recovery resources and vaccines during a public health crisis.

Understanding the urban life pattern of young people from delivery data

Young people are the backbone of urban development and an important pillar of social stability. The growth of young floating population in China has given rise to urban land expansion. Understanding the urban life pattern of urban life for young people benefits rational and effective land expansion. In this article, we introduce food delivery data into the process of exploring behavioral patterns of urban youth in Hangzhou, Zhejiang Province, China. The dynamic time warping (DTW) distance-based k-medoids method is applied to explore the main activity areas and activity patterns of the urban youth population. The results indicate that many young people from Hangzhou work in Internet companies, and most of work hotspot areas are observed in high-tech parks. The existence of overtime work is proved. Combined with the housing price data in Hangzhou, it is found that young people consider both housing prices and education environment when choosing where to live. The analysis combined with road network data reflects the planning characteristics of the city, also looks into differences between the actual city functions and the planning map. The proposed methods can provide useful guidance and suggestions for city planning.

Street life and pedestrian activities in smart cities: opportunities and challenges for computational urban science

Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

Influence of urban land-use change on cold-air path occurrence and spatial distribution

The urban population is predicted to reach a 70% share of global population by mid-century. Future urbanization might be directed along several development typologies, e.g. sprawling urbanization, more compact cities, greener cities, or a combination of different typologies. These developments induce urban land-use change that will affect urban climate and might reinforce phenomena such as the urban heat island and thermal discomfort of urban residents. A planning-based mitigation approach to ensure thermal comfort of residents are urban cold-air paths, i.e. low-roughness areas enabling drainage and transport of colder air masses from rural surroundings. We study how urban land-use change scenarios influence cold-air path occurrence probability and spatial distribution in a mid-European city using a machine learning approach, i.e. boosted regression trees. The Urban Sprawl Scenario results in the strongest reduction of cold-air path area by 3.6% in comparison to the reference case. The Green City Scenario gives evidence for an increase of cold-air path area (2.2%) whereas the Compact Green City Scenario partly counteracts the negative influence of urban densification by increased fractions of vegetated areas. The proposed method allows for the identification of priority areas for cold-air path preservation in urban planning.

“What should be computed” for supporting post-pandemic recovery policymaking? A life-oriented perspective

The COVID-19 pandemic has caused various impacts on people’s lives, while changes in people’s lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people’s lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people’s lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about “what should be computed?” in Computational Urban Science with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.

Latent factor analysis and measurement on sustainable urban livability in Siliguri Municipal Corporation, West Bengal through EFA and CFA model

United Nations’ Sustainable Development Goal targets to make cities and human settlements inclusive, safe, resilient, and sustainable; as it is predicting 95% urban expansion in the next decades. Consequently, urban livability can serve as a useful conceptual and analytical framework to improve the quality of urban life by facilitating the evaluation of the person–environment relationship and leading the improvement without deteriorating the environmental conditions. This present paper aims to identify the dimensions and indicators of subjective and objective livability for Siliguri Municipal Corporation (SMC). The residents’ perception has been carried out using stratified random sampling technique. Samples have been collected from the residents from each core, semi-periphery and peripheral areas of SMC. Mainly, adaptation of Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) model involves four livability dimensions; under which the overall model explains 65% of the total variance indicating with the high reliability (α > 0.7) and the Goodness-of-fit index (GFI) about 0.90. The result indicates that, ‘Accessibility Factor’ bears the highest impact (24.91%) among the four latent variables and ‘Socio-Economic’ factor has the lower impact (8.39%) upon the urban livability.

Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges

Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors’ opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source’s main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors’ research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.