Travel Impedance Research Articles

Accessibility of urban agglomeration integrated passenger transport network considering travel impedance

Accessibility of urban agglomeration integrated passenger transport network considering travel impedance

Trip Assignment Modeling for Transportation in Denpasar City Using PTV Visum Software

Mapping and macro transportation modelling are methods used to obtain an overview and analysis of transportation conditions at the macro level, encompassing all road segments in urban areas. Critical constraints on road segments can be identified through this process. Macro transportation modelling serves as a tool for studying the implementation of problem-solving policies, allowing the anticipated outcomes to be analysed through simulations in transportation modelling. This research has a specific goal: to understand the travel patterns of the population in the city of Denpasar. Calibration and validation processes in the PTV Visum modelling are conducted to test the validity of the created macrosimulation model. The trip assignment results are generated through PTV Visum modeling. The research methodology involves quantitative analysis using household interview surveys, traffic counting, correlational studies, and macrosimulation modelling. Calibration is performed by calculating the beta coefficient value (with specific calculated values), time matrices, and distance matrices to measure travel impedance functions. Validation is conducted by evaluating the Generalised Exponential Horn (GEH) value, which is less than 5, to ensure the alignment between travel output in the model and field survey data from traffic counting. From the analysis of a sample of 2,725 households, a total of 2,165,876 daily trips were obtained. Types of trips include movements from internal to internal zones (2,115,602 trips), from internal to external zones (28,799 trips), from external to internal zones (19,319 trips), and from external to external zones (2,159 trips) per day. Roads with the highest traffic volume in a single day include Hasanudin Street (42,524 pcu/hour), Diponogoro Street (42,282 pcu/hour), Sudirman Street (31,062 pcu/hour), Sutoyo Street (30,180 pcu/hour), and Sumatra Street (23,515 pcu/hour).

Access-based land value appreciation for assessing project benefits

The traditional mobility-oriented travel-time saving benefit assessment method has been repeatedly questioned for numerous intrinsic flaws, motivating the search for alternative benefit assessment approaches. Although a wealth of literature confirms the capitalization effect of access benefits induced by transport improvements to land or real estate value, the access-based land value uplift method hasn’t yet been widely recognized and employed as an official tool assisting transport decision-making. The present paper collects 136 empirical studies and aims to disentangle the obstacles hindering the promotion of the access-based assessment method by systematically reviewing methodological design, the access metrics used, and the target real estate sub-markets or land use types upon which access benefits are quantified. First, it was found that almost half of the sampled studies just investigated the general effects of transport operation on real estate prices without incorporating sufficient temporal and locational considerations, thereby failing to isolate project-specific incremental impacts. Second, while the hedonic pricing model remains the most popular model, a trend towards embracing more advanced modelling techniques such as spatial lag, spatial error, and Difference-in-Differences (DID) models to control for bias caused by spatial dependence has been observed. Third, Euclidean distance and distance buffer rings are the most widely used operational measures of access. Primal access measures covering the number of opportunities available at a destination and travel impedance are recommended. Last, over 86% of sampled empirical studies target the residential real estate market. The lack of non-residential land uses in the literature presents a significant research gap that should be addressed.

Gravity-based models for evaluating urban park accessibility: Why does localized selection of attractiveness factors and travel modes matter?

Gravity-based models have been extensively utilized in urban studies for measuring geographic disparities in access to urban parks over the past several decades. However, despite methodological advancements incorporating various aspects of accessibility, there has been limited focus on the impact of variable selection (e.g., attractiveness factors) and transport modes on accessibility evaluations. This study investigates the differences in gravity-based models for assessing park accessibility based on varying assumptions about attractiveness factors and travel impedance. Semi-structured interviews with local residents were conducted to identify the reasons for park visits in Shanghai. Our bivariate correlation analyses reveal that factors such as park openness and access to public transport were crucial, in addition to conventional factors identified in the literature (i.e., park size and driving accessibility). This insight led to the development of localized accessibility measurements that incorporate park inclusiveness (i.e., entrance fees and opening hours) and multimodal travel options (based on multinomial logistic mode choice models). The results indicate that the refined model produces lower and more varied accessibility levels, which can better capture accessibility gaps across different geographic contexts. This accurate and practical identification of accessibility gaps can assist local planners and decision-makers in formulating effective policies and strategies to promote equitable access to urban public parks.

Estimating geographic access to healthcare facilities in Sub-Saharan Africa by Degree of Urbanisation

Measuring rates of coverage and spatial access to healthcare services is essential to inform policies for development. These rates tend to reflect the urban-rural divide, typically with urban areas experiencing higher accessibility than rural ones. Especially in Sub-Saharan Africa (SSA), a region experiencing high disease burden amid fast urbanisation and population growth. However, such assessment has been hindered by a lack of updated and comparable geospatial data on urbanisation and health facilities. In this study, we apply the UN-endorsed Degree of Urbanisation (DoU or DEGURBA) method to investigate how geographic access to healthcare facilities varies across the urban-rural continuum in SSA as a whole and in each country, for circa 2020. Results show that geographic access is overall highest in cities and peri-urban areas, where more than 95% of inhabitants live within 30 min from the nearest HCF, with this share decreasing to 80–90% in towns. This share is lowest in villages and dispersed rural areas (65%), with about 10–15% of population more than 3 h away from any health post. Challenges in geographic access seem mostly determined by high travel impedance, since overall spatial densities of HCF are comparable to European levels.

Travel impedance, the built environment, and customized-bus ridership: A stop-to-stop level analysis

Travel impedance, the built environment, and customized-bus ridership: A stop-to-stop level analysis

A delicacy evaluation method for park walkability considering multidimensional quality heterogeneity

In the post-pandemic era, park walkability plays a vital role in promoting residents' physical and mental health in all aging societies. In current walkability calculation methods, the number of indicators reflecting park quality is limited, and the data resolution is too low to capture accurate park demand patterns. Moreover, the travel impedance based on the ArcGIS network cannot reveal the impedance differences between distinct road types. Therefore, a delicacy evaluation method for park walkability that considers multidimensional quality heterogeneity is required. Taking a residential compound as the unit, this study evaluated park walkability based on multi-source big data using the Huff-based Ga2SFCA (Gaussian two-step floating catchment area) model. The results suggest that, considering multidimensional quality heterogeneity, this method can accurately quantify the influence of facility improvement on park walkability, better describing the supply and demand relationship. Although the perfection of facilities can effectively improve park walkability, its influence on park distribution equity is small. To reduce the disparity in park distribution, the construction of new parks is necessary. A “pocket park” is a good option considering the limited land resource. Open campuses are good alternatives. Therefore, as an epidemic prevention policy, the “shut down policy” of campuses to the public does great harm to park walkability and the resulting equity, stopping the spillover of positive externalities of universities and colleges. This research not only improves the 2SFCA method from the indicator abundance and indicator counting approach in the supply dimension but also improves studies on park accessibility in terms of data accuracy and data resolution. This has important theoretical and practical significance for the delicacy management of urban areas.

How does trip frequency distort time value to impact congestion charging schemes?

Abstract A notable feature of a city or region with close economic and social connections with its neighbours is its highly mixed local and external traffic, and in some cases the external traffic volume is almost as high as that of local traffic. Whilst local traffic volume may be largely made up of the same regular local commuters making frequent trips, external traffic from outside of the city (region) may not be the same people making regular trips to/from the city. However, from a large pool of people making infrequent trips to/from the city, the existence of external traffic is proven by data from the licence plate recognition system of road vehicles in Changde, China. The function of value of time correlated with the income/wage rate and trip frequency is exploited and verified statistically. The time value distorted by trip frequency is defined as perceived time value (PTV), which also influences the way travellers perceive any travel impedance such as congestion delay and toll charges. This paper analyses the price of anarchy (POA) when explicitly considering the travel frequency of the trip-makers and their PTV, and compares with previous analysis without considering travel frequency. We show that when travel frequency is considered, the optimal toll of congested road pricing schemes which converts road traffic flow from user equilibrium into system optimization is much lower than that without considering travel frequency. The cost of licence plate auction cannot be treated as a congestion toll, which is only a threshold of vehicle ownership. That travellers choose routes by PTV rather than TV (time value) is proven by an example of Heishipu Bridge in Changsha, Hunan Province, China.

Bicycle Accessibility GIS Analysis for Bike Master Planning with a Consideration of Level of Traffic Stress (LTS) and Energy Consumption

Measuring the impact of bicycle infrastructure and other mobility improvements has been a challenge in the practice of transportation planning. Transportation planners are increasingly required to conduct complex analyses to provide supporting evidence for proposed plans and communicate well with both decision makers and the public. Cyclists experience two important factors on roads: (a) travel stress related to the built environment along with the traffic conditions and (b) changes in physical burden due to topography. This study develops a method that integrates an energy consumption calculation and “bicycling stress” score to take into account external conditions that influence cyclists substantially. In this method, the level of traffic stress (LTS) is used to select street segments appropriate for different comfort levels among cyclists and is combined with biking energy consumption, in addition to distance, which is used as travel impedance to consider the effects of slopes and street intersections. The integrated Geographic Information System (GIS) analysis methods are used to evaluate bicycle infrastructure improvements in the coming years in Montgomery County, MD, USA. The analysis results demonstrated that the infrastructure improvements in the county’s bike master plan are well-targeted to improve bicycling accessibility. Furthermore, the use of energy as opposed to distance to generate bikeshed areas results in smaller bikesheds compared to distance-generated bikesheds. The method presented herein allows planners to characterize and quantify the impact of bicycle infrastructure and prioritize locations for improvements.

Geographic Access to Primary Healthcare Services among Latinos/as/x in Western Alabama.

Alabama's Latino/a/x population grew 278% from 2000 to 2018. Tuscaloosa County, located in the largely rural region of western Alabama, also experienced a significant influx of Latino/as/x during this time frame. Geographic healthcare access (GHA) to primary care and hospitals is crucial for immigrant Latino/as/x to care for their health, but few studies have characterized it. The goals of this article were to describe the availability (defined as number of provider locations) and accessibility (defined as travel impedance between potential patients and provider locations) of primary healthcare services and to discuss potential strategies to address these healthcare access challenges. We drew data from the US Census Bureau, American Community Survey 5-year estimates, Blue Cross Blue Shield national doctor and hospital finder database, the Alabama Department of Public Health, and Tuscaloosa Transit Authority. We used geographic data, geographic information systems, and spatial analyses to characterize the availability and accessibility of primary care services and hospitals for Latinos/as/x in Tuscaloosa County using ESRI, ArcGIS 10.6.1. We showed the distribution of Latinos/as/x by census tract with choropleth mapping and mapped primary healthcare providers alongside public transit routes and hospital driving times to support our findings. This work demonstrated that Latinos/as/x in Tuscaloosa County were concentrated in more rural areas surrounding the county's city center, presenting significant barriers to GHA. These areas had fewer primary care providers and limited public transit. Many Latinos/as/x in this county had to travel ≥45 minutes to a hospital. Outreach and technology-based approaches, including home visit programs, mobile health units, and telemedicine, may be particularly important in bridging the GHA gaps for this and other largely rural populations the southeastern United States. Some of this potential was unlocked during the coronavirus disease 2019 crisis. These gains should be leveraged toward sustainable healthcare access initiatives for rural Latino/a/x populations.

Influence of in-vehicle crowding on passenger travel time value: Insights from bus transit in Shanghai, China

In-vehicle crowding is an external cost imposed on passengers by public transport. It is necessary to quantify the crowding discomfort to measure its influence on passengers’ travel choice. This study presents a stated preference survey embedded with revealed preference to investigate the influence of in-vehicle crowding on the travel choice behavior. The in-vehicle crowding degree is measured as standing density. Considering the in-vehicle environment and the subjective perception of passengers, the classification criteria for the in-vehicle crowding degree of bus transit are put forward. Then, the mixed logit model is used to establish the utility function of bus passenger travel choice behavior under different in-vehicle crowding degrees, and the value of travel time is calculated. Also, the functional relationship between in-vehicle crowding and travel time value is identified. In addition, by converting in-vehicle crowding into time, the travel impedance model considering in-vehicle crowding and the crowding value of time multipliers are defined. A study of Shanghai bus transit shows that when the standing density is higher than 7.5 passengers per square meter, bus passengers are willing to exchange 12.83 Chinese Yuan RMB for 1-hour travel time saving, and the perceived in-vehicle time is twice as long as the actual in-vehicle time. This study provides a reference for improving the attraction of bus transit and easing the decline in bus rides.

Effects of Social Vulnerability and Spatial Accessibility on COVID-19 Vaccination Coverage: A Census-Tract Level Study in Milwaukee County, USA.

COVID-19 vaccination coverage was studied by race/ethnicity, up-to-date doses, and by how it was affected by social vulnerability and spatial accessibility at the census-tract level in Milwaukee County, WI, USA. Social vulnerability was quantified at the census-tract level by an aggregate index and its sub-components calculated using the principal components analysis method. The spatial accessibility was assessed by clinic-to-population ratio and travel impedance. Ordinary least squares (OLS) and spatial regression models were employed to examine how social vulnerability and spatial accessibility relate to the vaccination rates of different doses. We found great disparities in vaccination rates by race and between areas of low and high social vulnerability. Comparing to non-Hispanic Blacks, the vaccination rate of non-Hispanic Whites in the county is 23% higher (60% vs. 37%) in overall rate (one or more doses), and 20% higher (29% vs. 9%) in booster rate (three or more doses). We also found that the overall social-vulnerability index does not show a statistically significant relationship with the overall vaccination rate when it is defined as the rate of people who have received one or more doses of vaccines. However, after the vaccination rate is stratified by up-to-date doses, social vulnerability has positive effects on one-dose and two-dose rates, but negative effects on booster rate, and the effects of social vulnerability become increasingly stronger and turn to negative for multi-dose vaccination rates, indicating the increasing challenges of high social vulnerability areas to multi-dose vaccination. The large negative effects of socio-economic status on the booster rate suggests the importance of improving general socio-economic conditions to promote multi-dose vaccination rates.

Beyond Proximity: Utility-Based Access from Location-Based Services Data.

Simple SummaryProximity to community resources is often used as a benchmark in spatial public health analyses, but a measure that incorporates observed preferences leads to different policy interventions.Understanding who in a community has access to its resources—parks, libraries, grocery stores, etc.—has profound equity implications, but typical methods to understand access to these resources are limited. Travel time buffers require researchers to assert mode of access as well as an arbitrary distance threshold; further, these methods do not distinguish between destination quality attributes in an effective way. In this research, we present a methodology to develop utility-based accessibility measures for parks, libraries, and grocery stores in Utah County, Utah. The method relies on passive location-based services data to model destination choice to these community resources; the destination choice model utility functions in turn allow us to develop a picture of regional access that is sensitive to: the quality and size of the destination resource; continuous (non-binary) travel impedance by multiple modes; and the sociodemographic attributes of the traveler. We then use this measure to explore equity in access to the specified community resources across income level in Utah County: the results reveal a discrepancy between which neighborhoods might be targeted for intervention using space-based analysis.

Metric, Topological, and Syntactic Accessibility in Three-Dimensional Urban Networked Spaces: Modeling Options and Visualization

In this paper, we take the position that cities gain to be represented as three-dimensional spaces populated by scores of micro-scale-built spaces (buildings, rooms, passageways, squares, etc.). Effective algorithms that evaluate place-based accessibility in built structures while considering the indoor spaces’ complexity at a fine granularity are essential for indoor–outdoor seamless urban planning, navigation, way findings, and supporting emergencies. We present a comprehensive set of spatial modeling options and visualizations of indoor accessibility for an entire built structure based on various notions of travel impedance. Notably, we consider the metric length of the paths and their cognitive complexities due to topologic, syntactic, or integrated intricacy within our approaches. Our work presents a comprehensive selection of indoor accessibility analysis with a detailed implemental discussion that can be applied as a solid foundation for smart city applications or seamless urban research and planning. The analysis and visualization techniques presented in this paper can be easily applied to analyze and visualize built interior geographic spaces to study accessibility differentials in cities with vast vertical expansion aimed at achieving (or at avoiding) specific accessibility outcomes.

Applications of Passive GPS Data to Characterize the Movement of Freight Trucks—A Case Study in the Calgary Region of Canada

The movement of trucks represents a significant portion of travel. Surveys have traditionally been used to measure truck movement, but this costly and limited data collection method typically involves in-person interviews and requiring high workload. This study explores different ways in which passive truck GPS data can be used to complement traditional data collection methods, for obtaining detailed information about the travel behaviors of freight trucks. First, we develop a heuristic-based model to identify truck stops. A new methodology is proposed to classify truck stops into a primary or secondary sto, which make identifying trip purposes possible. Primary stops are defined as the locations where the loading or unloading of the goods takes place. Secondary stops are those associated with all other purposes, including refueling, and driver breaks. Finally, we develop a destination choice model for modeling truck movements. This model applies a discrete choice modeling technique to distribute truck trips within the Calgary region, Canada. We test the utility function in the destination choice model to include of business establishment data, travel impedances, and other dummy variables that are likely to influence truck demand. The results show that a combination of trucks’ dwelling times and their entropy can be used to classify truck stops by purpose. This study also shows the potential of using passive GPS data to gain additional insights into truck movements characterization and truck trip distribution modeling.

Entropy Tucker model: Mining latent mobility patterns with simultaneous estimation of travel impedance parameters

With the rapid increase in the availability of passive data in the field of transportation, combining machine learning with transportation models has emerged as an important research topic in recent years. This study proposes an entropy Tucker model that integrates (1) a Tucker decomposition technique, i.e., an existing machine learning method, and (2) an entropy maximizing model, i.e., an existing model used for modeling trip distribution in the field of transportation. In addition, an optimization algorithm is presented to empirically identify the proposed model. The proposed model provides a solid theoretical foundation for the machine learning method, substantially improves prediction performance, and provides richer behavioral implications through empirical parameter estimation of travel impedance. We conducted a case study using public transit smart card data. The results showed that the proposed model improves the prediction performance and interpretability of the results compared to the conventional nonnegative Tucker decomposition technique. Further, we empirically confirmed that the travel impedance varies with the origin–destination pair, time of the day, and day of the week. Finally, we discussed how embedding the theoretical foundations of transport modeling into machine learning methods can facilitate the use of various passive data in wider practical contexts of transport policy decision making.

Sharing behavior in ride-hailing trips: A machine learning inference approach

Ride-hailing is rapidly changing urban and personal transportation. Ride sharing or pooling is important to mitigate negative externalities of ride-hailing such as increased congestion and environmental impacts. However, there lacks empirical evidence on what affect trip-level sharing behavior in ride-hailing. Using a novel dataset from all ride-hailing trips in Chicago in 2019, we show that the willingness of riders to request a shared ride has monotonically decreased from 27.0% to 12.8% throughout the year, while the trip volume and mileage have remained statistically unchanged. We find that the decline in sharing preference is due to an increased per-mile costs of shared trips and shifting shorter trips to solo. Using ensemble machine learning models, we find that the travel impedance variables (trip cost, distance, and duration) collectively contribute to 95% and 91% of the predictive power in determining whether a trip is requested to share and whether it is successfully shared, respectively. Spatial and temporal attributes, sociodemographic, built environment, and transit supply variables do not entail predictive power at the trip level in presence of these travel impedance variables. This implies that pricing signals are most effective to encourage riders to share their rides. Our findings shed light on sharing behavior in ride-hailing trips and can help devise strategies that increase shared ride-hailing, especially as the demand recovers from pandemic.

Integrating fuzzy set function into floating catchment area measures: a determination of spatial accessibility of service points

ABSTRACT The use of discrete (binary or zonal/step-wise) instead of continuous travel impedance functions in floating catchment area (FCA) measures generates inconsistent and less reliable accessibility indices that might be overestimating or underestimating actual accessibility. Thus, this study illustrates the limitations of discrete travel impedance function in the enhanced two-step (E2S) FCA method; and develops a fuzzy-based continuous travel impedance function by combining the degree to which travel distances belong to different sub-zones of catchment areas and their respective E2SFCA zonal weights using the weighted mean fuzzy-set operation. With the developed fuzzy-based travel impedance function, a new accessibility measure, fuzzy-based two-step (F2S) FCA is defined. The E2SFCA and F2SFCA measures were implemented in the hypothetical set up and Rural Wards of Dodoma Urban District in Tanzania to determine spatial accessibility of service points and water points, respectively. The resulting E2SFCA and F2SFCA accessibility indices portrayed a slightly similar pattern. However, F2SFCA accessibility indices varied among households within and across sub-zones, while E2SFCA accessibility indices were identical within but changed abruptly across sub-zones. The variations in F2SFCA accessibility indices reflect the reality and to a greater extent Tobler’s first law of Geography. Thus, the developed F2SFCA measure could be used to measure spatial accessibility of other services or commodities as it properly models travel impedance. The F2SFCA measure could further be improved to generate much more realistic accessibility indices by capturing local instead of total potential demand, commonly modelled in FCA measures.

Research on Optimization of Urban Public Transport Network Based on Complex Network Theory

The urban public transportation system is an important part of urban transportation, and the rationality of public transportation routes layout plays a vital role in the transportation of the city. Improving the efficiency of public transportation can have a positive impact on the operation of the public transportation system. This paper uses complex network theory and the symmetry of the up and down bus routes and stations to establish an urban public transit network model and calculates the probability of passengers choosing different routes in the public transit network according to passenger travel impedance. Based on passenger travel impedance, travel path probability and passenger travel demand, the links are weighed, and the network efficiency calculation method is improved. Finally, the public transit network optimization model was established with network efficiency as the objective function and solved by the ant colony algorithm. In order to verify the effectiveness of the model and the solution method, this paper selects areas in Nanguan District of Changchun City for example analysis. The result shows that the efficiency of the optimized network is 8.5% higher than that of the original network, which proves the feasibility of the optimized model and solution method.

A Study of the Factors Affecting Multimodal Ridesharing with Choice-Based Conjoint Analysis

As public perception about the shared economy evolves, peer-to-peer ridesharing has been gaining increased attention worldwide. Both private and public sector entities have launched mobile app-based ridesharing services, while a range of methodologies and system architectures have been proposed in academia. Whereas traditional ridesharing methods match drivers and riders when their origin and destination are similar, recently proposed algorithms often feature multi-hop and multimodal properties that allow riders to be connected by multiple modes. Such algorithms can reduce travel time and/or travel cost; however, they may also add other travel impedances, such as requiring multiple transfers. Understanding user behavior toward such new ridesharing systems is essential for successful service design. For policymakers and service planners, identifying factors that impact traveler choices can lead to better design and improved services. This research involved a web-based survey to capture traveler preferences using a conjoint analysis framework. A choice-based method was adopted to identify factors for the estimation model and to analyze traveler willingness to pay. Among the proposed factors, the number-of-transfers was shown to be the most important, as was expected. When a multimodal ridesharing system provides less travel time, low travel cost, and sufficient ridesharing incentive, people are more likely to pay for the service.