Transportation Analysis Zones Research Articles

Study on the Influence of the Built Environment and Personal Attributes on Commuting Distance: A Case Study of the Tianjin Central Area Divided by TAZ Units

Long commuting distances pose a significant challenge for many large cities, undermining the principles of sustainable urban development. The factors influencing urban commuting distances among residents are complex and necessitate hierarchical analysis. This study uses Tianjin, one of China’s four municipalities, as a case study, employing transportation analysis zones (TAZ) as research units. We classify these units based on resident and working populations, extracting multiple built environment and personal attribute factors to establish a model that examines the influence of the job–housing balance. The analysis identifies 12 sub-items across two categories of influencing factors, with correlations tested through spatial analysis and linear regression. We found 28 positive associations and 35 negative associations. Notably, the job–housing relationship for the working population was generally more sensitive to changes than that of the resident population. At the TAZ level, personal attributes exerted a more significant influence on the job–housing balance than built environment factors, with commuting mode, life stage, age, and income level notably affecting commuting distances.

A scoping review on cycling network connectivity and its effects on cycling

ABSTRACT There is growing recognition of the importance of a well-connected cycling infrastructure. Common connectivity measures apply aggregate area-based metrics, such as the number of streets per transportation analysis zone or census area. However, such measures capture cyclist movement insufficiently, as cyclist movement easily extends beyond areas, and variations in the sizes and shapes of areas lead to inconsistent outcomes. In contrast to area-based connectivity, network connectivity considers the connectivity of the entire cycling network, capturing the continuity or discontinuity of routes. Certain measures can account for other important aspects, such as topology and the relative importance of individual links in large networks. Although previous reviews have covered several aspects of cycling infrastructure, no review has been dedicated to the measures, methods and models applied to assess network connectivity or the impact of increased network connectivity on cycling behaviour. We conduct a scoping review of measures, methods, models, and data sources to assess cycling network connectivity and review associations between network connectivity and travel behaviour. The findings suggest an increase in the number of publications on network connectivity up to 2019, followed by a plateau in the number of studies but with more complex methods. However, that complexity may constitute a barrier to practical implementation. We conclude that empirical verifications regarding the effects of network connectivity on travel behaviour remain a research gap, even in high-cycling countries, with evidence further limited by limited link-level travel data. Future research should also focus on developing more consistent and validated metrics.

Multiscale Urban Functional Zone Recognition Based on Landmark Semantic Constraints

The classification of urban functional areas is important for understanding the characteristics of urban areas and optimizing the utilization of urban land resources. Existing related methods have improved accuracy. However, they neglect cognitive differences amongst humans in the different scales of regional functions. Moreover, how to build the correlations of cross-scale characteristics is still unresolved when realizing the classification of multiscale urban functional zones. To resolve these problems, a transportation analysis zone involving urban buildings as research units is created and these units are described by geometric and functional characteristics using multiple data sources. Then, a hierarchical clustering model is built for the recognition of urban functional areas at varying scales with landmark semantic constraints. In the experiments, Shanghai served as the study area, and multiscale zones were created using different levels of road networks considering the constraint correlation of the significance between cross-scale maps. The experiential results show the proposed method has excellent performance and optimizes the functional zone classification at different scales. This study not only enriches the multiscale urban functional area-recognition methods but also can be used in other aspects, like cartographic generalization or spatial analysis.

Principles of Allocation of Special Transportation Analysis Zones

The article discusses the principles of the formation of the transportation analysis zones, taking into account special generators of trips that are understood as large objects of mass service of the population, in relation of which, the generation of trips is comparable to the generation of certain districts of the city. The conditions for the allocation of such generators in the special transportation analysis zones are considered. The methodology to form the initial data in relation to the transportation analysis zones using modern geographic information systems technologies is described. The classification of the transportation analysis zones is presented.

Planning of integrated mobility-on-demand and urban transit networks

We envision a multimodal transportation system where Mobility-on-Demand (MoD) service is used to serve the first mile and last mile of transit trips. For this purpose, the current research formulates an optimization model for designing an integrated MoD and urban transit system. The proposed model is a mixed-integer non-linear programming model that captures the strategic behavior of passengers in a multimodal network through a passenger assignment model. It determines which transit routes to operate, the frequency of the operating routes, the fleet size of vehicles required in each transportation analysis zone to serve the demand, and the passenger flow on both road and transit networks. A Benders decomposition approach with several enhancements is proposed to solve the given optimization program. Computational experiments are presented for the Sioux Falls multimodal network. The results show a significant improvement in the congestion in the city center with the introduction and optimization of an integrated transportation system. The proposed design allocates more vehicles to the outskirt zones in the network (to serve the first mile and last mile of transit trips) and more frequency to the transit routes in the city center. The integrated system significantly improves the share of transit passengers and their level of service in comparison to the base optimized transit system. The sensitivity analysis of the bus and vehicle fleet shows that increasing the number of buses has more impact on improving the level of service of passengers compared to increasing the number of MoD vehicles. Finally, we provide managerial insights for deploying such multimodal service.

Modeling county-level benzene emissions using transportation analysis zones in the Denver metro area

The link between exposure to benzene (an important toxic air pollutant) and physical health is well-documented as benzene is a known carcinogen. Understanding benzene concentration distributions in urban areas is very important and can be done using air dispersion models. An essential input requirement for dispersion models is the spatial and temporal allocation of emissions. Since most county-level benzene emissions come from gasoline powered motor vehicles, the goal of this work was to develop a new method of county-level benzene emission allocation using transportation analysis zones (TAZs) commonly used in travel demand models. The high spatial resolution provided by the TAZ allocation method helped visualize different flux patterns generated from five emission sectors and identified where more effort is needed to reduce benzene concentrations. The new allocation method was also tested using the AERMOD dispersion model to estimate benzene concentrations in the Denver metropolitan area. The model performance was assessed using 2014 morning benzene measurements from a monitoring station located in downtown Denver; results showed an acceptable model-to-monitor ratio. The model was also used to investigate the effect of temperature inversions on morning benzene concentrations. As expected, predicted benzene concentrations were higher during temperature inversions and model-to-monitor ratios during investigated days were within a factor of two. The TAZ allocation modeling methodology demonstrated in this work can improve the estimation of air pollution exposure in future health-related studies. • County-level benzene emissions were allocated based on transportation analysis zones. • This new allocation method identified on-road, non-road, and oil and gas as major emission sources. • Urban benzene concentrations were reasonably predicted using the updated emission allocations and a dispersion model. • Model-predicted and measured benzene concentrations were elevated during morning temperature inversions.

Multiscale Effects of Multimodal Public Facilities Accessibility on Housing Prices Based on MGWR: A Case Study of Wuhan, China

The layout of public service facilities and their accessibility are important factors affecting spatial justice. Previous studies have verified the positive influence of public facilities accessibility on house prices; however, the spatial scale of the impact of various public facilities accessibility on house prices is not yet clear. This study takes transportation analysis zone of Wuhan city as the spatial unit, measure the public facilities accessibility of schools, hospitals, green space, and public transit stations with four kinds of accessibility models such as the nearest distance, real time travel cost, kernel density, and two step floating catchment area (2SFCA), and explores the multiscale effect of public services accessibility on house prices with multiscale geographically weighted regression model. The results show that the differentiated scale effect not only exists among different public facility accessibilities, but also exists in different accessibility models of the same sort of facility. The article also suggests that different facilities should adopt its appropriate accessibility model. This study provides insights into spatial heterogeneity of urban public service facilities accessibility, which will benefit decision making in equal accessibility planning and policy formulation for the layout of urban service facilities.

Impacts of shared autonomous vehicles: Tradeoff between parking demand reduction and congestion increase

Shared autonomous vehicles (SAVs) can have significant impacts on the transport system and land use by replacing private vehicles. Sharing vehicles without drivers is expected to reduce parking demand, and as a side effect, increase congestion owing to the empty fleets made by SAVs picking up travelers and relocating. Although the impact may not be uniform over a region of interest owing to the heterogeneity of travel demand distribution and network configuration, few studies have debated such impact at a local scale, such as in transportation analysis zones (TAZs). To understand the impact in relation to geographical situations, this study aims to estimate the impacts of SAVs at the local scale by simulating their operation on a developed simulator. Using the mainland of Okinawa, Japan as a case study, it was found that parking demand was reduced the most in residence-dominant zones in terms of quantity and office-dominant zones in terms of proportion. As a side effect of replacing private vehicles with SAVs, empty fleets increase congestion, particularly at the periphery of the city. Overall, the results show the heterogeneous impacts of the SAVs at the TAZ level on both land use and traffic, thus suggesting the importance of developing appropriate strategies for urban and transport planning when considering the characteristics of the zones.

A clustering method for measuring accessibility and equity in public transportation service: Case study of Melbourne

This paper proposes a Modified Cumulative Accessibility (MCA) measure incorporating actual travel demand information (Origin-Destination trip patterns) to improve the accuracy of the measure as a reliable proxy for equity evaluation and amelioration in accessing Public Transportation (PT) services. The travel demand information is reflected into the MCA measure by evaluating and clustering PT and Private Vehicle Transportation (PVT) trip diaries. Both vertical and horizontal equities are measured for the network's Transportation Analysis Zones (TAZs). To redress the inequities, a priority list of operational recommendations for each TAZ is identified. The proposed method is tested with the Greater Melbourne area datasets and the TAZs with the highest inequity along with prescribed operational recommendations are identified. In terms of equitable distribution of public transportation service, a major observation from the case study model was that the outer suburban areas located in the eastern side of the the Melbourne metropolitan area are relatively underserved as compared to the inner and western suburbs.

Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

Multimodal transportation system protection against sea level rise

Transportation infrastructure resilience is an important component of a region’s ability to recover from natural disasters. While Sea Level Rise (SLR) is becoming inevitable with climate change, little is known of the impact of protection strategies on multiple modes of transport. The present paper proposes a framework where a range of coastal protection strategies are undertaken in the case of one meter of SLR (expected by the year 2100). The methodology incorporates high-resolution hydrodynamic simulations using the Coastal Storm Modeling System (CoSMoS) and traffic simulations using the Multi-Agent Transport Simulation (MATSim) to quantify the potential impact of SLR and protection strategies on both the highway and public transit systems in the San Francisco Bay Area.The results of the traffic simulations are analyzed at the regional and Transportation Analysis Zone (TAZ) levels. Modeling results show that coastal protection of one area will affect the transportation system (sometimes negatively) in areas beyond its vicinity. The improved spatial resolution, and the integration of highway and transit networks in a unified model, reveal transportation phenomena that were not identified in previous studies. By quantifying the impacts on commuters’ mobility in different TAZs, the methodology can be used to develop effective and inclusive strategies against SLR for a given region of interest.

Detecting latent urban mobility structure using mobile phone data

The spatial heterogeneity of land use patterns and residents’ corresponding economic activities give rise to urban mobility’s latent structure, which is of great importance for urban planning and transport infrastructure investment but cannot be readily captured using conventional data sources. We developed a methodological framework for detecting urban mobility structure at the transportation analysis zone (TAZ) level in Beijing using mobile phone signal data. First, we derived origin–destination data at the TAZ level from mobile phone data and visualized them in ArcGIS. Next, we improved community detecting algorithms generally used in social networks by reversing distance weight, such as by dividing ODs by 1, and used the results to reveal hidden clustering features of TAZs, according ODs between them. We visualized and analyzed population density, OD spatial distribution at different times, and ratio of daytime to nighttime population using the GIS platform; all showed some spatial cluster features. We then applied a structure detection algorithm using ODs between TAZ pairs to identify the hidden structure of urban mobility extracted from phone data. For Beijing, the identified mobility structure contains 27 clusters, with those in suburban areas tending to match administrative boundaries well but those in the developed center areas showing complex distributions and matching administrative boundaries poorly. Authorities that provide mobility infrastructure can use the resulting insights into urban planning and transportation planning to inform policy decisions at the local and city levels.

A case study in spatial-temporal accessibility for a transit system

We show the impact on transit network connectivity of a major network redesign using a comprehensive connectivity measure. This measure captures the quality of service impact – the difference between the actual accessibility and the designed accessibility as a function of space and time. The former is influenced by on-time performance and ability to make transfers. The measure is unique in that it incorporates both spatial and temporal aspects of the transportation network, so that the effects of the network geometry, transportation services schedules, and operational performance (in the form of service reliability / schedule adherence) are all appropriately reflected in this unified measure. We employ a spatial statistical model to tie aggregate ridership to average connectivity across transportation analysis zones. The statistically significant relationship indicates that, at least in aggregate, connectivity and demand are linked. Using spatio-temporal clustering, we show where, when, and how accessibility is impacted by a significant network redesign.

Four-step travel demand model implementation for estimating traffic volumes on rural low-volume roads in Wyoming

ABSTRACTThis study develops a four-step travel demand model for estimating traffic volumes for low-volume roads in Wyoming. The study utilizes urban travel behavior parameters and processes modified to reflect the rural and low-volume nature of Wyoming local roads. The methodology disaggregates readily available census block data to create transportation analysis zones adequate for estimating traffic on low-volume rural roads. After building an initial model, the predicted and actual traffic volumes are compared to develop a calibration factor for adjusting trip rates. The adjusted model is verified by comparing estimated and actual traffic volumes for 100 roads. The R-square value from fitting predicted to actual traffic volumes is determined to be 74% whereas the Percent Root Mean Square Error is found to be 50.3%. The prediction accuracy for the four-step travel demand model is found to be better than a regression model developed in a previous study.

PRINCIPLES OF TRANSPORT ANALYSIS ZONES IN URBAN TRANSPORT PLANNING

The work is devoted to the urban transportation planning, transportation modeling and problems of collecting the necessary input data. The principles of Transportation Analysis Zone forming are developed, taking into account the special urban trips generators. Techniques of the input data for Transportation Analysis Zone using modern GIS technology are described. The classification of Transportation Analysis Zone is presented.

Accessibility dynamics and location premia: Do land values follow accessibility changes?

The structure of transportation networks and the patterns of accessibility they give rise to are an important determinant of land prices, and hence urban spatial structure. While there is ample evidence on the cross-sectional relationship between location and land value (usually measured from the value of improved property), there is much less evidence available on the changes in this relationship over time, especially where location is represented using a disaggregate measure of urban accessibility. This paper provides evidence of this dynamic relationship using data on home sales in the Minneapolis-St Paul, MN, USA metropolitan area, coupled with disaggregate measures of urban accessibility for multiple modes, for the period from 2000 to 2005. Our investigation tracks the effects of marginal changes in accessibility over time, as opposed to static, cross-sectional relationships, by using an approach in which the unit of observation is a ‘representative house’ for each transportation analysis zone in the region. This approach allows us to control for changes in structural attributes of houses over time, while also isolating the effect of changes in accessibility levels. Results of this approach are compared with a cross-sectional model using the same variables for a single year to illustrate important differences. Empirical estimates indicate that while most of the models estimated using a cross-sectional specification yield positive and significant effects of accessibility on sale prices, these effects disappear when the models are transformed into first-difference form. We explain these findings in light of the state of maturity of urban transportation networks.

Representing pedestrian activity in travel demand models: Framework and application

There have long been calls for better pedestrian planning tools within travel demand models, as they have been slow to incorporate the large body of research connecting the built environment and walking behaviors. Most regional travel demand forecasting performed in practice in the US uses four-step travel demand models, despite advances in the development and implementation of activity-based travel demand models. This paper introduces a framework that facilitates the abilities of four-step regional travel models to better represent walking activity, allowing metropolitan planning organizations (MPOs) to implement these advances with minimal changes to existing modeling systems. Specifically, the framework first changes the spatial unit from transportation analysis zones (TAZs) to a finer-grained geography better suited to modeling pedestrian trips. The MPO's existing trip generation models are applied at this spatial unit for all trips. Then, a walk mode choice model is used to identify the subset of all trips made by walking. Trips by other modes are aggregated to the TAZ level and proceed through the remaining steps in the MPO's four-step model. The walk trips are distributed to destinations using a choice modeling approach, thus identifying pedestrian trip origins and destinations. In this paper, a proof-of-concept application is included to demonstrate the framework in successful operation using data from the Portland, Oregon, region. Opportunities for future work include more research on the potential routes between origins and destinations for walk trips, application of the framework in another region, and developing ways the research could be implemented in activity-based modeling systems.

Considering Public Transit: New Insights into Job and Healthy Food Access for Low-Income Residents in Baltimore, Maryland

Scholars, policymakers, and activists widely accept that low-income neighborhoods tend to lack amenities such as healthy food and employment opportunities. Most studies concerning access to these amenities assume that residents either drive a personal vehicle or walk to their destination, yet many low-income urban residents rely on public transit systems to access supermarkets and employment. This is especially true in Baltimore, a city where 11% of households do not own a personal vehicle. This study provides a methodological approach to incorporate these concerns into spatial analyses of food deserts and employment access. The method uses metropolitan planning organization-defined transportation analysis zones (TAZ) as the unit of analysis and evaluates public transit access to healthy food (i.e., supermarkets) and employment opportunities from each TAZ. This analysis can be used by decision makers to identify areas for improvement and ensure that transit-dependent individuals have good access to employment and healthy food sources. Twenty-four TAZs are identified as high-priority for transit improvements, many of which include former industrial areas and company towns, demonstrating the impact of historical development patterns on current populations in Baltimore. Considering budget constraints of transit agencies, this methodological approach can be used to make cost-effective and impactful transit improvements that address the needs of transit-dependent residents.

Direct transportation economic impacts of highway networks disruptions using public data from the United States

ABSTRACTThis article presents a sequential method to estimate the direct transportation economic impacts (DTEI) related to transportation due to disruptions in highway networks used by trucks and cars. The main input is the Freight Analysis Framework version 3, best public data for truck movements in the United States. The method considers multicommodity flows in an equilibrium framework, associates monetary values to changes in traffic conditions that are specific to each user type, and links truck flows with commodity flows. This approach can consider transportation analysis zones smaller than those presented in the Freight Analysis Framework. A real-world numerical example is presented to estimate the DTEI due to severe floods that occurred in 2008 and disrupted key segments of the highway network in the northwestern Indiana region.

A spatio-temporal approach for identifying the sample size for transport mode detection from GPS-based travel surveys: A case study of London’s road network

Compared with conventional household one/two days travel survey, GPS-based travel surveys hold many attractive features for travel behaviour studies. Different machine learning-based techniques have been developed to infer the transportation mode based upon GPS data from such surveys. However, nearly none of these studies calculate the sample size required for validating these techniques. Since different surveys target different study areas for different temporal periods and different travel modes, identifying sample sizes for all transport modes at different spatio-temporal granularities is of imperative urgency given the high time and financial costs of GPS-based travel surveys. Here we use road network journey time data of London to calculate appropriate sample sizes for travel surveys designed either for a specific period-of-the-day, day-of-the-week or month-of-the-year. We also use different transportation analysis zones (central, inner and outer London) to demonstrate the spatial variability of the data over these different survey durations. Then we finally calculate and analyse the range of required sample sizes for different travel modes within these spatio-temporal granularities. This case study provides a good reference of sample size design for GPS-based travel survey in big cities.