Evaluating the Vulnerability and Impact Zones of Urban River-Crossing Corridors: A Case Study of Wuhan, China

Development of zone system for macro-level traffic safety analysis

Developing reliable collision prediction models (CPMs) at the Traffic Analysis Zones (TAZs) aggregation level requires accurate assignment for boundary geocoded data between adjacent TAZs. Traffic Analysis Zones (TAZs) are spatial divisions within a region commonly used for traffic analysis purpose. The boundaries of TAZs are frequently set to match the centerline of major roadway segments. Collision counts data has shown that significant proportion of collisions occur frequently on these major roadways. Consequently, the way in which collisions, and also other geocoded data, along TAZs' boundaries are assigned into adjacent zones is of interest because it has direct impact on the prediction ability of macro-level CPMs. In this study, data for 422 TAZs from the City of Ottawa was used to develop macro-level CPMs. Geocoded data on TAZ's boundary were assigned between adjacent TAZs using ten different assignment methods. Negative binomial regression (NB) was applied to develop CPMs for total, nonfatal injury, property damage only (PDO), bike-involved, and pedestrian-involved collisions. Many explanatory variables expected to have an effect on the roadway safety performance at the TAZ's level were aggregated to the TAZ's level. These independent variables were categorized into four data categories including roadway characteristics, socio-economic and demographic characteristics, exposure, and Transportation Demand Management (TDM) variables. In addition, Zero-inflated regression was used to model fatal collisions as a function of Vehicle Kilometre Travelled (VKT) and total lane kilometre (TLKM). Results of the developed models show that different geocoded boundary data assignment methods do affect the accuracy of developed CPMs results significantly. It was found that allocating boundary data to TAZs evenly improved model iii results significantly. The results also indicate that most of relevant independent variables have provided results consistent with previous studies. ix

The effects of traffic zoning with regular geometric shapes on the precision of trip production models

Urban spatial elements present agglomeration and dispersion geographic processes in the urban development. Identifying the characteristics of their distribution changes and accurately capturing the evolution of the urban spatial structure is of great significance to urban construction and management. This study takes Wuhan as a case study and focuses on the spatial agglomeration distribution of urban elements. Point of Interest (POI) data from 2017 to 2021 were collected, and the Block2Vec model was employed to extract the comprehensive geographic information from various elements within the traffic analysis zones (TAZs). Subsequently, identification and division were carried out to access the level of urban spatial element agglomeration. Finally, the spatial–temporal evolution characteristics of urban aggregated elements in the Wuhan metropolitan development area over five years were compared and analyzed. The results indicate the following: (1) urban elements present an obvious circle structure in their spatial agglomeration, with distinct differences observed among different element types; (2) from 2017 to 2021, the Wuhan urban development zone experienced obvious expansion in urban space; (3) increased agglomeration of spatial elements mainly occurred in the surrounding areas of the city, while some areas in the city center displayed weaker element agglomeration and a reduction in various service facilities. The results demonstrate that the method used in this study could effectively identify the spatial agglomeration distribution of urban elements, as well as accurately distinguishing regions with distinct development characteristics. This approach could provide robust support for optimizing land use and urban spatial planning.

A new zone system to analyze the spatial relationships between the built environment and traffic safety

Combining public transportation (PT) with Bike-Sharing Systems (BSSs) offers a pathway toward the sustainable development of urban mobility. These systems can reduce fuel consumption, air pollution, and street congestion, especially during peak hours. Moreover, PT and BSS are frequently used by individuals without access to private vehicles, including low-income groups and students. Whereas increasing PT network infrastructure is constrained by issues such as high capital costs and limited street space (which inhibits mass transit options like BRT or trams), BSS can be used as an adaptable and affordable solution to fill these gaps. In particular, BSS can facilitate the “first-mile–last-mile” legs of PT journeys. However, many transit agencies still rely on traditional joint service planning and overlook BSS as a critical mode in integrated travel chains. This paper proposes that PT and BSS be considered as a unified network and introduces a framework to assess whether access to this integrated system is equitably distributed across urban areas. The framework estimates demand for travel using public mobility options and supply at the level of Traffic Analysis Zones (TAZs), treating PT and BSS as complementary modes. Spatial accessibility analysis is employed to examine connectivity using factors that affect access to both PT and BSS. The proposed approach is tested by taking Tehran as the focus of the case analysis. The results identify the most accessible areas and highlight those that require improved PT-BSS integration. These findings provide policy-relevant suggestions to promote equity and efficiency in urban transport planning. The outcomes reveal that central TAZs in Tehran receive the highest level of PT-BSS integration, while the western and southern TAZs are in urgent need of adjustment to ensure better distribution of integrated public transportation and bike-sharing services.

Collective and individual spatial equity measure in public transit accessibility based on generalized travel cost

Spatial-interaction network analysis of built environmental influence on daily public transport demand

How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation

Understanding the relationship between the demand for public transportation and land use is critical to promoting public-transportation-oriented urban development. Taking Beijing as an example, we took the Public Transportation Index (PTI) during the working day’s early peak hours as the dependent variable. And 15 land use and built environment variables were selected as the independent variables according to the “7D” built environment dimensions. According to the Modifiable Areal Unit Problem (MAUP), the size and shape of the spatial units will affect the aggregation results of the dependent variable and the independent variables. To find the ideal spatial unit division method, we assess how well the nonlinear model fits several spatial units. Extreme Gradient Boosting (XGBoost) was utilized to investigate the nonlinear effects of the built environment on PTI and threshold effects based on the ideal spatial unit. The results show that (1) the best spatial unit division method is based on traffic analysis zones (TAZs); (2) the top four explanatory variables affecting PTI are, in order: mean travel distance, residential density, subway station density, and public services density; (3) there are nonlinear relationships and significant threshold effects between the land use variables and PTI. The priority regeneration TAZs were identified according to the intersection analysis of the low PTI TAZs set and the PTI-sensitive TAZs set based on different land use variables. Prioritized urban regeneration TAZs require targeted strategies, and the results of the study may provide a scientific basis for proposing strategies to renew land use to increase PTI.

Several large-scale person trip surveys include the information of the origin and destination of the trip only at the TAZ (traffic analysis zone) level, so the accuracy of location information is not enough to examine the effect of access and egress conditions on mode choice. Two approaches are applied in this study to complement the imprecise information; one for access to public transit from home, and the other for egress from public transit to destination. Home-based trip data with the destinations as university, governmental office, and hospitals are used in this study. About the information of the egress, the precise location of the destination are identified within TAZ from GIS database using the purpose of the trip and the type of the destination reported by the respondent, and the distance from the nearest train station and bus stop are calculated. About the access to the public transit form home, the distance from home to the public transit is treated as a probabilistic variable in estimating the mode choice model in this study. The model has the same structure as the latent class model. Census data which contain the population distribution within TAZ at city block level is used for the distribution of origin. The results of empirical analysis show that the proposed model has a better log-likelihood at convergence than those with TAZ centroids as the ends of the trip. The results suggest that the proposed model has the same effect as obtaining the precise location information, and that it enables to better represent mode choice behavior than using TAZ centroid. The results also suggest that imprecise location information provides smaller coefficient estimates for the effect of access and egress conditions, resulting the underestimate on the elasticity of the access and egress conditions for promoting public transit.

Public transit (PT) has always been essential to urban mobility. Although numerous metrics and assessments have been established for evaluating PT, they have focused solely on internal aspects of PT. However, PT systems do not operate independently, they face competition from multiple modes, resulting in a decrease in PT ridership. Therefore, external metrics are needed to capture the development level and appeal to passengers. This study proposed a framework to evaluate PT supply gap by using the travel attributes difference between ride-hailing (RH) and PT as external metrics. Taking Xi’an as the case study, the traffic analysis zones (TAZs) were segmented utilizing K-means clustering and multiple Thiessen polygons, and the PT supply score was determined by the independent weight–technique for order preference by similarity to ideal solution (TOPSIS) model. Furthermore, the PT supply gap was obtained by integrating the PT demand data. By analyzing the relationship between RH-PT differences and PT supply, we set external standards for evaluating PT development. The findings show a spatial pattern in the distribution of evaluation metrics, with the inner-city regions excelling the outer ones, and the western sections outperforming the eastern ones. The regions displaying lower PT supply scores are predominantly located in the city’s eastern outskirts. The PT supply gap is distributed both in the core and outskirts of the city, with walking distance being the primary contributing factor. The evaluation methods and external standards proposed by the study complement the existing evaluation system and support transportation managers in enhancing the attractiveness of PT.

It's well known that collecting traffic information by cell phone CEL-ID location technique-firstly get subscribers `travel routine by analyzing and retrieving CELL-ID number uploaded by cell phone customers, then other necessary information (e.g. Origin-Destination Flow, the number of Residents and Jobs) gained after father technical processing. However, since all of these indicators are based on traffic analysis zone (TAZ) unit and restricted by the accuracy of CELL-ID location method and communication theory, the size of the traffic analysis zone has significant impact on the accuracy of indictors. For example, the relative error of the indicator will increase if the traffic analysis zone is divided too small. But if traffic analysis zone is divided too big, the trip distribution characteristic won't be presented clear. That's why this dissertation is to contribute a reasonable methodology of solving traffic analysis zone size based on CELL-ID location technique.

In a four-step traffic planning model, the division of traffic analysis zones is primarily based on motorized traffic travel. Therefore, there is a need to study the none-motorized traffic zones. The paper defines the pedestrian traffic analysis zone, summarizes the principles of the pedestrian traffic zone division and proposes a division method based on pedestrian attracting/producing points. The division method includes three steps. Initially, the service radiuses of attracting/producing points are determined and unit zones are formed. Then, an envelope is established for the unit zones which have overlapping areas. Eventually, according to division principles, the boundary of the pedestrian traffic analysis zone is determined. A method of pedestrian travel intensity estimation of the traffic analysis zones is also proposed in this paper which is helpful for determining the pedestrian priority hierarchy and developing pedestrian priority measures.

Road traffic carbon dioxide (CO2) has a fundamental role in global warming. Accurately estimating and understanding the spatio-temporal patterns of urban road traffic CO2 emissions plays a fundamental role in developing targeted reduction strategies. However, few studies have estimated CO2 emissions at the urban road scale with fine spatio-temporal resolution. Therefore, this study adopted a bottom-up method to estimate urban road traffic CO2 emissions using Global Positioning System (GPS) trajectory data. Urban road traffic CO2 emissions from individual vehicles are estimated using a vehicle trajectory-driven CO2 emission model The aggregated results are mapped within Traffic Analysis Zones (TAZ) to create CO2 emission distribution maps with minute-level temporal and road-level spatial resolution. The Multiscale Geographically Weighted Regression (MGWR) model is employed to analyze the impact of various elements of the built environment on urban road transport CO2 emissions. Experimental results indicate that road traffic CO2 emissions in Hangzhou have spatio-temporal heterogeneity. Road traffic CO2 emission hotspots are concentrated along main roads such as Shixiang Road, the City Ring Expressway, and Shiqiao Road. Further analysis indicates that population density, main road density, availability of bus stops, and length of bike lanes exert a significant influence on urban road transport CO2 emissions in Hangzhou. These findings enhance our recognition of the combined effects of the various elements of the built environment on urban road transport CO2 emissions. This study introduces a method for estimating CO2 emissions at the street level using vehicle trajectory information. It provides high spatio-temporal resolution CO2 emission distribution maps to support carbon emissions reduction strategies in urban transportation.