Traffic Analysis Zones Research Articles

A user-based approach for assessing spatial equity of attractiveness and accessibility to alternative urban parks

ABSTRACT Spatial equity in access to urban services is a significant element of sustainable urban planning. To date, a service-based index is used to measure service attractiveness, considering attractiveness as a static concept. While from the user side view, service attractiveness is a dynamic concept related to the variation of different opportunities of users to use the services and capacities. In this paper, a user-based method for measuring service attractiveness is proposed. In this approach, the attractiveness of service for a user is modeled as a function of the user’s Travel Times (TT), Free Times (FTs), park size and relative positions of the user, local demanding population, and service. Then, the inequity of attractiveness of services for different users has been examined using three inequity indices including the Duration of Use (DU), Frequency of Visit (FV), and the Chance of Space Choice (CSC). The proposed method is applied to urban regional parks of Tehran municipality. Traffic Analysis Zones (TAZs) and regional parks are respectively regarded as the users and services. In addition to considering inequity of access to the first closest park, the additional cost of accessing to the other two closest parks is also considered. Results show that the proposed method leads to the exploration of the existing inequities of DU, FV, and CSC among residents in local scales, whereas in the service-based method, all users of a service are highlighted with a single value of attractiveness. Comparison of the user-based and service-based attractiveness indices show significant differences. As an example, user-based attractiveness values of about 14% and 20% of TAZs are respectively 5 and 3 square meters more than that of the service-based attractiveness index. However, the service-based method is suitable for regional-scale comparison of the inequities. Therefore, the proposed index can complement the service-based index for inequity mapping and management in varying scales.

Post COVID-19 pandemic recovery of intracity human mobility in Wuhan: Spatiotemporal characteristic and driving mechanism

After successfully inhibiting the first wave of COVID-19 transmission through a city lockdown, Wuhan implemented a series of policies to gradually lift restrictions and restore daily activities. Existing studies mainly focus on the intercity recovery under a macroscopic view. How does the intracity mobility return to normal? Is the recovery process consistent among different subareas, and what factor affects the post-pandemic recovery? To answer these questions, we sorted out policies adopted during the Wuhan resumption, and collected the long-time mobility big data in 1105 traffic analysis zones (TAZs) to construct an observation matrix (A). We then used the nonnegative matrix factorization (NMF) method to approximate A as the product of two condensed matrices (WH). The column vectors of W matrix were visualized as five typical recovery curves to reveal the temporal change. The row vectors of H matrix were visualized to identify the spatial distribution of each recovery type, and were analyzed with variables of population, GDP, land use, and key facility to explain the recovery driving mechanisms. We found that the “staggered time” policies implemented in Wuhan effectively staggered the peak mobility of several recovery types (“staggered peak”). Besides, different TAZs had heterogeneous response intensities to these policies (“staggered area”) which were closely related to land uses and key facilities. The creative policies taken by Wuhan highlight the wisdom of public health crisis management, and could provide an empirical reference for the adjustment of post-pandemic intervention measures in other cities.

Inferring origin and destination zones of transit trips through fusion of smart card transactions, travel surveys, and land-use data

This paper presents a data fusion method to infer the origin and destination zones of transit trips from smart card data. The fusion framework has disaggregate mixed multinomial logit models at its core that predict the most probable origin and destination zones of individual transit trips using smart card transaction records, land use data, and transit system characteristics. The logit models are estimated using revealed trip origin and destination responses from a travel survey that are augmented by land use and transit system data to provide contexts about the zones' trip generation and attraction potentials. For empirical analysis, the methodology is applied to the smart card system of the Greater Toronto and Hamilton Area. Specifically, it is tested under different system configurations (tap-on-only and tap-on-and-off) and for networks with substantial shares of automobile and walk access/egress. When applied to transit trips constructed from the smart card transactions, the estimated models successfully capture the spatial distribution of trip origin and destination at the traffic analysis zone level. The empirical analysis also demonstrates that the proposed fusion method can be appropriately used to reconcile information provided by transit smart card and travel surveys to generate up-to-date transit demand data necessary for public transport planning and operations.

Implementing a Ride-sharing Algorithm in the German National Transport Model (DEMO)

As macroscopic travel demand models are widespread among regional modelers, it is crucial to develop extensions for new mobility services such as ride-sharing services to assess their effects on the transport system. In a feasible approach that was introduced with previous research, origin-destination network-based paths are transformed to a series of crossed traffic analysis zones (TAZs). These series are compared to find overlapping trajectories to derive ride-sharing potential. However, in national transport models the TAZ sizes are larger than in regional models, which can lead to unrealistically high matching rates because of imprecise path trajectories. This paper presents a modified approach for implementation in the German national transport model, DEMO. The main adjustment is to use population centers as pick-up locations instead of TAZs. Each TAZ includes multiple points. This way, the trajectories of paths can be better differentiated. Another advantage of this method is that intrazonal trips can be distributed between the centers and thus be included in the algorithm. With the successful implementation in DEMO, the effects of new mobility tools in German cities can be evaluated within the same model.

A Framework for Measuring the Spatial Equity in the Distribution of Public Transportation Benefits

This paper proposes that an equitable transit system requires that the geographical distribution of transit service benefits conform to the geographical distribution of the citizens with the greatest need for public transportation. This is the essence of vertical equity. This study calculated “connectivity power,” which reflects public transit service quality in each traffic analysis zone (TAZ) in a city to indicate the amount of benefit that TAZ is receiving from the transit system. The number of carless citizens in each TAZ was also calculated as an index of need to the public transit services in that area. Conformity of need and supply was analyzed using Spearman’s rank correlation and the Gini index. This framework indicates that in Isfahan, Iran, adding three bus rapid transit (BRT) lines and completing the existing metro line would not improve the Gini index but would improve the Spearman’s rank correlation to an acceptable level.

Investigating mandatory and non-mandatory trip patterns based on socioeconomic characteristics and traffic analysis zone features using deep neural networks

Forecasting travel demand is a classic problem in transportation planning. The models made for this purpose take the socioeconomic characteristics of a subset of a population to estimate the total demand, mainly using random utility models. However, with machine learning algorithms fast becoming key instruments in many transportation applications, the past decade has seen the rapid development of such models for travel demand forecasting. As these algorithms are independent of assumptions, have high pattern recognition ability, and often offer promising results, they can be effective alternatives to discrete choice models for forecasting trip patterns. This paper aimed to predict mandatory and non-mandatory trip patterns using a Deep Neural Network (DNN) algorithm. A dataset containing Metropolitan Washington Council of Government Transportation Planning Board (MWCGTPB) 2007–2008 survey data and a dataset containing traffic analysis zones’ characteristics (TAZ) were prepared to extract and predict these patterns. After the modeling phase, the models were evaluated based on accuracy and Cohen’s kappa coefficient. The estimates of mandatory and non-mandatory trips were found to have an accuracy of 70.87% and 50.02%, respectively. The results showed that a DNN could find the relationship between socioeconomic factors and trip patterns. This can be helpful for transportation planners when they are trying to predict travel demand.

A crash feature-based allocation method for boundary crash problem in spatial analysis of bicycle crashes

In conventional safety analysis, traffic and crash data are often aggregated at the geographical units like census tracts, street blocks, and traffic analysis zones, which are often delineated by roads and other physical entities. A considerable proportion of crashes may occur at or near the boundary of geographical units. Such the crashes, also known as boundary crashes, can correlate with the explanatory variables of neighboring geographical units, regardless of the spatial proximity. This could then bias the parameter estimation of crash frequency model. In this study, a novel data-driven approach is developed for the allocation of boundary crashes. For example, crash severity and bicyclist characteristics are considered in the crash feature-based allocation. An illustrative case study based on built environment, population, traffic and bicycle crash data from 289 Lower Layer Super Output Areas (LSOAs) of London in the period 2017–2019 was conducted. Results indicate that high matching percentages of boundary crash allocation can be achieved. Furthermore, prediction performances, in terms of root mean square error (RMSE) and mean absolute error (MAE), of the crash frequency models based on the proposed crash feature-based allocation method is superior, compared to that based on conventional boundary crash allocation methods like half-and-half and iterative assignment approaches. Last but not least, more influencing factors that affect the bicycle crash frequency at macroscopic level can be identified. Findings should be indicative to the spatial safety analysis for different geographical configurations.

The influence of traffic-infrastructure factors on pedestrian accidents at the macro-level: The geographically weighted regression approach

Introduction: Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies. Method: This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance. Results: This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area. Conclusion: These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces. Practical Applications: The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity.

Uncovering Taxi Mobility Patterns Associated with the Public Transportation Shutdown Using Multisource Data in Washington, D.C.

The relationship between taxi travel patterns and public transportation disruption has not been extensively explored. In this study, we investigated the impact of public transportation disruption on the taxi mobility patterns during the metro shutdown in Washington, D.C.. Multiple data source, involving taxi trips, traffic analysis zone, and point of interest (POI) information, was collected to compare the taxi travel patterns before, during, and after the metro shutdown. The number, distance, and duration of taxi trips were found to be significantly higher during the metro shutdown; specifically, the number of taxi trips was found to be 19.8% larger. Furthermore, a POI auxiliary analysis was performed to investigate the variation in community structure during the disruption of public transport using the modularity maximization approach. The results of this study will be useful for the development of taxi scheduling strategies and traffic management.

How to locate urban–rural transit hubs from the viewpoint of county integration?

Chinese new urbanization requires the integration of urban and rural transportation, especially at the county level. Establishing a reliable and practical method to locate urban–rural transit hubs becomes a key issue in transportation planning. To solve the issue that existing methods lack applicability in practices due to the need for costly data collection, this study develops an application-oriented model. First, we generate basic nodes with traffic analysis zones in both urban and rural areas. Second, the node importance evaluation model is established to identify potential hub zones based on socioeconomic and topological factors. Third, we optimize the final hub locations to maximize the served population and urban transit accessibility. To reflect passenger characteristics, accessibility to opportunities is defined in terms of weighted land-use areas within a walking distance. ArcGIS software is applied to improve the efficiency of data processing and to improve the accuracy of the calculated network distances. A case study is conducted in Gaoping, China, to confirm the validity of the proposed approach. The results indicate that the optimal hub locations are effectively aligned with the practical conditions, thus improving travel convenience. The proposed approach has the potential for broader use by transport planners and policymakers in developing people-oriented and integrated urban–rural transit hubs.

An alternative method for assessing urban transportation carrying capacity

Urban transportation is a complex system typically composed of car, bus, non-motorized, and metro travel modes, which connects various residential traveling and transport facilities. It is important to have a proper method to facilitate understanding on urban transportation carrying capacity (UTCC) of individual types of travel modes and the balance performance between the carrying capacities offered by different travel modes. This study introduces an alternative model named “load-carrier” perspective method for assessing the four types of UTCC. A measurement of coordination degree is used to measure if the carrying capacities between different types of travel modes are developed and utilized in balance. An application of the proposed method is conducted by using empirical data collected from 805 traffic analysis zones (TAZs) in Chongqing City in China in 2020. The research findings suggest that: 1) the UTCC performance can be divided into five grades, namely, low, mild, moderate, high, and extremely high level of utilization intensity. 2) most of the TAZs in Chongqing were in low and mild level of utilization intensity of UTCC. 3) the balance performance between four types of transportation carrying capacities was poor in Chongqing City, with 97.1% of the sample TAZs are in imbalanced and slight imbalanced status. The demonstration conducted proves that the introduced method provides an important tool for examining the UTCC performance by different travel modes.

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

Traffic analysis zone (TAZ) is the basic unit of urban transportation planning. The appropriateness of TAZ delineation will affect the rationality of transportation planning and the accuracy of transportation analysis, and thus affect the final planning decision-making. However, in the big data era, there is still a lack of methods to integrate multi-source data for TAZ delineation. How to effectively fuse multi-source heterogeneous data in the TAZ delineation process is still an unsolved technical difficulty. To fill this gap, this paper designs a multi-source data-driven stepwise strategy to solve the TAZ delineation problem by creating a zoning system with zones showing homogeneous mobility behaviors and containing homogeneous land use characteristics. Firstly, mining the spatial-temporal travel features and land use information of transit stations and parcels from multi-source data, including transit smart card data, ride-hailing data, bike-sharing trip data, and point-of-interest (POI) data. Then, a core parcel determination algorithm which mainly consists of Fuzzy C-Means station clustering and a constructed parcel core degree function is proposed to generate the core parcels of each potential TAZ. After that, parcels are clustered around the core ones into TAZs through a multi-feature driven clustering algorithm, in the process of which the homogeneity within TAZs is guaranteed. Finally, the optimal zoning system is obtained by comparing the information loss calculation result of multiple zoning schemes. Taking Beijing as a case study area, 624 TAZs are obtained by applying the proposed method. The delineation result is comparatively analyzed with TAZs obtained by the traditional delineation method and a baseline method by applying multi-indicator measurement. The result reveals that the presented approach can promote the rationality of TAZ delineation.

The Impact of Rainfall on Urban Human Mobility from Taxi GPS Data

Rainfall severely impacts human mobility in urban areas and creates significant challenges for traffic management and urban planning. There is an urgent need to understand the impact of rainfall on residents’ travels from multiple perspectives. Taxi GPS data contains a large amount of spatiotemporal information about human activities and mobility in urban areas. For this study, we selected the central area of Zhuhai as the study area and used taxi data from August 2020 for the investigation. Firstly, we divided the taxi data into four scenarios, i.e., weekdays with and without rainfall and weekends with and without rainfall and analyzed and compared the trip characteristics for the different scenarios. Then, using the traffic analysis zone (TAZ) as the node and taxi flow between TAZs as edges, we constructed a network and compared the network indicators under the different scenarios. Finally, we used the Leiden algorithm to detect communities in different scenarios and compared the network indicators of the communities. The results showed that on days with rainfall, taxi flow and its spatial and temporal distribution pattern changed significantly, which affected transportation supply and demand. These findings may provide useful references for the formulation of urban transport policies that can adapt to different weather conditions.

Refined Urban Functional Zone Mapping by Integrating Open-Source Data

The determination of a reasonable spatial analysis unit is an essential step in urban functional zone (UFZ) division, which significantly affects the results. However, most studies on the division of functional zones are based on excessively large spatial units, such as blocks or traffic analysis zones (TAZs), which easily overlook the detailed characteristics of urban regions and introduce bias to the research conclusion. To address this issue, a refined zone segmentation method, namely, the Voronoi diagram for the polygon method, was proposed to generate refined spatial analysis units. Afterward, the functional topics of the spatial analysis unit were classified by a multiclass support vector machine (SVM) to produce the final UFZ map, where the functional topics of each spatial unit were obtained by coupling latent Dirichlet allocation (LDA). To verify the effectiveness of the proposed method, experiments were conducted in Beijing, China. The results indicated that the proposed segmentation method can generate fine-scale spatial units and provide fine-grained and higher accuracy UFZs (overall accuracy = 84%; kappa = 0.82).

How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis

During rapid urbanization, the optimization of the built environment in metropolises to promote the use of public transportation considerably eases the road traffic pressure. Researchers usually adopt census or survey data and global models, which inadequately represent huge population and reflect the spatial non-stationarity. This study collected the location-based service data to identify commuters and their commuting modes and aggregated the share of transit commuting to traffic analysis zones (N = 586). While controlling three socio-economic attributes (age, gender & income), the 5Ds (Density, Diversity, Design, Distance to transit, and Destination accessibility) was formed to describe the built environment (BE) in Wuhan. Then we mainly compared the regression results of the geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). Although the GWR model could reveal the spatial non-stationarity by local fitting, its regression coefficients with fixed bandwidth (BW = 153) tended to show high variability with abnormal sign reversals due to overfitting. The application of MGWR model with adaptative bandwidths could further consider the scale effect and provide robust estimation results. According to the various bandwidths provided by MGWR, the policies to improve the BE had been discussed in three scales. We emphasize that policies should be formulated based on the spatial distribution of the coefficients, that is, “Adapt to local conditions”.

Integrating macro and micro level crash frequency models considering spatial heterogeneity and random effects

Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro–macro level model for crash frequency estimation. Specifically, the study develops an integrated model system that allows for the influence of independent variables at the microscopic level to be incorporated within the macroscopic propensity estimation. The empirical analysis is based on the data drawn from 300 traffic analysis zones, 1818 roadway segments, and 4184 intersections from the City of Orlando, Florida for the years 2018 and 2019. The study considers a host of exogenous variables including roadway and traffic factors, land-use, built environment, and sociodemographic characteristics for the model estimation. The proposed model system can also accommodate for hierarchical correlations such as correlation between all segments or intersections in a zone. The study findings highlight the presence of common spatial unobserved factors influencing crash frequency across segment level and intersection level as well as presence of significant parameter variability across both micro and macro level in the crash frequency. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated micro–macro model relative to the non-integrated macro model. The overall model fit measures and interpretations encourage the application of the proposed model for crash frequency analysis.

Evaluation of Medical Carrying Capacity for Megacities from a Traffic Analysis Zone View: A Case Study in Shenzhen, China

Sustainable Development Goals propose to build inclusive, safe, resilient, and sustainable cities and human settlements, which requires us to scientifically evaluate the carrying capacity of current urban public service facilities, but there is still a lack of in-depth exploration of urban public medical service facilities. Therefore, this paper, within the mobile phone signaling data, improved the potential model and carrying capacity evaluation model of public medical facilities, explored the spatial pattern distribution of public medical resources in Shenzhen, and analyzed the current situation of carrying capacity of public medical resources. The study showed that: (1) the overall spatial distribution of public medical resources in Shenzhen is uneven, showing a pattern of multicenter aggregation and multilevel development; (2) the service potential of public medical facilities has obvious spatial variations, with Futian District, Dapeng New District, and Nanshan District showing more obvious high-gravitational-value aggregation centers; (3) medical facilities in Shenzhen are never empty, but the problems of medical underloading and overloading are severe, and spatial allocation and utilization efficiency need to be further optimized. The research results can provide a scientific basis for the research on the allocation and sustainable construction of medical resources in megacities.

Association of the built environment with motor vehicle emissions in small cities

This paper elucidates the association between built environments and motor vehicle emissions in small cities based on a geography weighted regression (GWR) model and multi-source fusion data from the urban area of Holingola, China. It finds that all emissions are spatially autocorrelated and spatially clustered throughout whole traffic analysis zones (TAZs). In addition, the GWR model bears more weight in measuring the local link when compared to the ordinary least squares model. What’s more, the estimated coefficients of built environment characteristics vary positively or negatively in diverse TAZs. The findings demonstrate that the impact of built environment characteristics on motor vehicle emissions is significantly spatially heterogeneous. These results are not only conducive to a better comprehension of how built environment factors affect motor vehicle emissions, but also help to establish policies and strategies to promote energy conservation and emissions reduction in small cities.

Bayesian multivariate spatial modeling for crash frequencies by injury severity at daytime and nighttime in traffic analysis zones

ABSTRACT This paper develops a multivariate spatial model for the joint analysis of daytime and nighttime crash frequencies by injury severity in traffic analysis zones (TAZs). The model specification allows for spatial correlation across TAZs, heterogeneous effects specific to crash time and severity, and correlations across response types under a Bayesian multivariate conditional autoregressive framework. One hundred and thirty one TAZs in Hong Kong, China, with traffic crash, traffic flow, roadway network, and land use data for a one-year period are selected to calibrate the advocated model. Considerable spatial and heterogeneous effects are found for each type crash frequency. Significant correlations exist in the heterogeneous effects for various severity levels and those for daytime and nighttime. The Bayesian estimates of the regression coefficients reveal that there are significant inconsistencies in the set of factors contributing to zonal daytime and nighttime crashes at various severity levels.

Measuring Cluster-Based Spatial Access to Shopping Stores under Real-Time Travel Time

Shopping stores are an important part of retail facilities and indispensable public facilities in a city. They are not only concentrated in shopping malls, but also distributed independently throughout the city, and often agglomerated in space. This paper attempts to measure the rationality of the spatial layout of all shopping stores in the city. Residents will visit multiple shopping stores in one trip to meet their demands. Based on this characteristic, this paper studies shopping store clusters and proposes a cluster two-step floating catchment area (C-2SFCA) method to analyze the accessibility differences of shopping stores in urban areas. Using the case of Beijing within the Fifth Ring Road, this paper implements the C-2SFCA method in a study unit of traffic analysis zones (TAZ) considering three transport modes (car, public transport, walking) with the support of real-time travel time collected from an internet map. The results show that spatial accessibility differed greatly under different transport modes and also had an uneven distribution pattern. Among these three results, the spatial variation of public transport accessibility was the highest. The results can provide references for urban planners in facility configuration and decision-making.