Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

A Mixed Geographically Weighted Regression (GWR) model is applied to explore the effects of shared mobility trips on taxi and public transit ridership at the macro-level. Several essential variables, including socioeconomic, transportation, network, and land use data, are set as the causal factors. The experiment is conducted using the smart card data, vehicle GPS trajectories, and vehicle order data collected in Shenzhen City, China. We show that the Mixed GWR outperforms the basic GWR in model fitting and capturing the unobserved heterogeneity. The spatial analysis reveals that bike-sharing addresses the “last-mile” and “first-mile” problems to bus and metro in the urban periphery. It substitutes the bus and taxis in short-distance journeys in the city center. However, the over-placement of bike-sharing in some regions limits the flexibility of bike-sharing connections to the metro. In the city center, ride-hailing fills the gaps in bus coverage and competes with the metro. In the peripheral areas, ride-hailing replaces buses and improves the accessibility to metro stations. The transportation policy increases the cooperation between ride-hailing and taxis citywide, although competitions in few regions need to be solved. The abovementioned results provide policy suggestions to optimize the allocation of local transportation resources.     

Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou,China

Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multi-source spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use.     

The interplay between dockless bikeshare and bus for small-size cities in the US: A case study of Ithaca

As another mode of shared transportation, bikeshare can substitute or complement public transit. Prior studies mainly relied on self-reported survey data or aggregated station-level data from docked bikeshare systems, and their conclusions and implications were focused on large cities. It is largely unknown how and to what extent a dockless bikeshare system complements or substitutes public transit, especially in small cities. This study was set to measure the interplay between Lime dockless bikeshare and bus service in Ithaca, NY – a typical small-size college town – and its environs. By joining about 3.42 million records of bus stop data and 102 thousand Lime bikeshare trip data from 2019, two types of Bikeshare-Bus-Linkage (BBL) trips were identified, namely (1) the first-mile trip where a user rides a Lime to board a bus, and (2) the last-mile trip where a user bikes to their destination after alighting a bus. BBL trips were identified using a spatiotemporal proximity framework based on two important parameters: the catchment radius and the time window between a bus stop event and a Lime trip. Different values were tested with a sensitivity analysis, and the parameters were finally set at 100 ft. and 5 min. As such, 3026 BBL trips were identified, which was 3% of total Lime ridership or 0.1% of total bus ridership. Our findings indicated that Lime provided useful first- and last-mile transfers to bus service for commuters. The complementary effect was particularly strong in the urban core and with transit development and employment land use areas. Moreover, in the morning peak, there were more first-mile trips from residential areas to bus stops in the urban core, while in the evening peak more last-mile trips started from bus stops in the urban core to residential areas. Based on the unique first-mile and last-mile trip patterns identified, policy implications and recommendations for bikeshare operators, local government, transit agencies, and transportation policymakers were discussed to better integrate bikeshare and public transit.     

Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression

Geographically weighted regression (GWR) models have been employed in previous studies regarding vehicular travel demands, but few studies have locally modeled walking travel demands at intersections to address the issue of spatially varying relationships. Harnessing a comprehensive collection of walking and bicycling traffic counts over 10 years in Chittenden County, Vermont, US, along with socioeconomic characteristics, transit accessibility indices, land use attributes and characteristics of intersections and roadway networks, this study utilizes GWR models to identify whether there are spatially varying relationships between active mode travel demands and ambient built-environment attributes. One Ordinary Least Square (OLS) model and two GWR models were parametrically calibrated: a full GWR model of all local variables and a mixed GWR model of both global and local variables. K-fold cross-validation method is used to select variables that significantly influence the volume of active travel modes in the OLS model. The uniform set of variables is investigated in two GWR models. Only residuals of the mixed GWR model exhibit spatial independence. The prediction accuracy of the three models is respectively compared by means of the k-fold cross-validation method. Results show that the mixed GWR model has higher prediction accuracy, while the other two models have roughly the same level of performance. We find that not all independent variables possess a spatially varying relationship with active mode volumes. The flexibility of the mixed GWR model that allows some independent variables to be global strengthens its prediction power. With these findings, transportation planners can dynamically estimate bicycle and pedestrian volumes at widespread intersections, and this geographical realism would facilitate local transportation planning, facility design, safety enhancement and operation analysis, as well as instilling new insights into interdisciplinary spatial research domain.     

Analysis on spatiotemporal urban mobility based on online car-hailing data

As an emerging travel mode, online car-hailing plays an increasingly important role in people's daily travel. Car-hailing data provide a new source to study human mobility in urban areas. This study focuses on identifying the distribution of regions with high travel intensity and the correlation between travel intensity and points of interest (POIs), based on the online car-hailing data collected in Chengdu, China. Firstly, the whole city area was divided into 16,100 uniform blocks and the number of pick-up and drop-off activities in each block was counted. Then, all POIs were categorized into 13 types and the number of different types of POIs in each block was counted. On this basis, the grade of travel intensity and POIs density in each block was identified according to the number of travel activities and POIs respectively. Finally, the correlation between the travel intensity and the POIs density was explored with ordered logistic regression. Experiment results showed that regions with high travel intensity are mainly distributed within the Second Ring Road, while those in the suburbs of city are usually the large transportation hubs, such as airports and train stations. Different types of POIs have different impacts on the online car-hailing travel intensity, and the density of traffic facilities has the greatest impact, including pick-up and drop-off, followed by density of scenic spot. The densities of service facilities and sports facility have an impact on the intensity of pick-up, while the impact on the intensity of drop-off is not significant. The density of company has no significant impact on the intensity of neither pick-up nor drop-off. These findings can contribute to a better understanding of online car-hailing travel activities and their relation with the urban space, and can provide useful information for urban planning and location-based services.     

Near “real-time” estimation of excess commuting from open-source data: Evidence from China's megacities

Urban commuting has continuously fascinated scholars and decision-makers. As few people live and work in the same place, there is always excess commuting (i.e., the non-optimal or surplus work travel occurring in cities because people do not minimize their journeys to work for most residents). Traditional commuting data sources (e.g., questionnaires and census surveys) are challenged by small samples, high cost, and low spatiotemporal resolution. In contrast, the big social-sensing data (e.g., smart card and mobile phone data) only consider one or two traffic mode of a route, which is not consistent with the real-life condition. This article proposes a framework for modeling excess commuting based on open-source data of the ten most populous megacities in China. We downloaded residential points of interest (POIs) from Lianjia Real Estate website and obtained workplace POIs from China's AMAP, which is widespread used as Google map. The stratified sampling approach was employed to derive commuting pairs. Both commuting distance and time were obtained by the shortest path under public transportation from AMAP. Then, the linear programming method was employed to calculate the theoretical minimum commuting time and distance of each city. We analyzed the statistical property and spatial distributions of excess commuting and found that (1) commuting distances and time (ranging from 9.1 to18.1 km and from 44.8 to 74.3  minutes) of all ten megacities follow a left-skewed normal distribution; (2) in terms of commute cost, all cities show universal core-periphery patterns where the spatial heterogeneity of the commuting time is more significant than that of distance; (3) for each city, the excess commuting measured by time (i.e. from 0.61 to 0.79) is lower than that measured by distance (i.e. 0.68 to 0.89); and (4) the role of mixing land use, waterbody distribution, and centripetal urbanization on urban commuting distance and time is significant.     

Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan,China

Many countries have implemented public bike systems to promote sustainable public transportation. Despite the rapid development of such systems, few studies have investigated how built environment factors affect the use of public bikes at station level using trip data, taking account of the spatial correlation between nearby stations. Built environment factors are strongly associated with travel demand and play an important role in the success of public bike systems. Using trip data from Zhongshan's public bike system, this paper employed a multiple linear regression model to examine the influence of built environment variables on trip demand as well as on the ratio of demand to supply (D/S) at bike stations. It also considered the spatial correlations of PBS usage between nearby stations, using the spatial weighted matrix. These built environment variables mainly refer to station attributes and accessibility, cycling infrastructure, public transport facilities, and land use characteristics. Generally, we found that both trip demand and the ratio of demand to supply at bike stations were positively influenced by population density, length of bike lanes and branch roads, and diverse land-use types near the station, and were negatively influenced by the distance to city center and the number of other nearby stations. However, public transport facilities do not show a significant impact on both demand and D/S at stations, which might be attributed to local modal split. We also found that the PBS usage at stations is positively associated with usage at nearby stations. Model results also suggest that adding a new station (with empty capacity) within a 300 m catchment of a station to share the capacity of the bike station can improve the demand-supply ratio at the station. Referring to both trip demand models and D/S models, regression fits were quite strong with larger R² for weekdays than for weekends and holidays, and for morning and evening peak hours than for off-peak hours. These quantitative analyses and findings can be beneficial to urban planners and operators to improve the demand and turnover of public bikes at bike stations, and to expand or build public bike systems in the future.     

Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model

With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DASs), it becomes possible in recent to explore the determinants of urban taxi ridership using multi-source heterogeneous data. This paper aims to use floating car data, points-of-interests (POIs) data and housing-price data to assess the influence of the built environment on taxi ridership. Within a scale of 0.5 km grid, critical indicators related to the economic aspect, intermodal connection, and land use factors were obtained using the multi-source data in Shanghai. To capture the spatial and temporal heterogeneity, Semi-parametric Geographically Weighted Poisson Regression (SGWPR) models are built over different time dimensions. It is found that SGWPR models result in higher goodness-of-fit than the generalized linear models. More importantly, the results show the impacts of built environment factors on taxi demand are highly heterogeneous, positive or negative in different city areas, reflected in the significant temporal variations of the effects. Overall, these findings suggest that the built environment factors have significant impacts on urban taxi demand, and the spatial context should not be ignored. Findings in this paper are expected to help better understand the relationship between urban taxi demand and built environment factors, improving the service level of the urban taxi system, and offering valuable insights into future urban and transportation planning.     

Discovering temporal and spatial patterns and characteristics of pavement distress condition data on major corridors in New Mexico

Roadway networks, as part of transportation infrastructure, play an indispensable role in regional economies and community development. The high-quality pavement serviceability of these networks is essential to ensure safe, cost-effective daily traffic operations. In-depth analyses of network-wide pavement surface condition data are necessary inputs for optimal pavement design and maintenance, traffic safety enhancement, and sustainable traffic infrastructure system development. This study aims to investigate various pavement distress condition performance measurements and their correlations to better understand temporal–spatial characteristics of roadway distress based on pavement distress condition data collected in New Mexico from 2006 to 2009. Eight major corridors across various urban and rural areas were selected for analyzing pavement surface-distress conditions and discovering their intrinsic characteristics and patterns across both temporal and spatial domains. The results show that there are not strong correlations among different distress measurements, implying the rationality of the current pavement performance measurement protocol used by the state transportation agencies. Regression models were established and GIS-based spatial analyses were performed to extract temporal and spatial patterns of Distress Rate (DR) data. The model results illustrate significant correlations of the DR data on the same route between two consecutive years, which can be partially characterized by a Markov process. GIS-based spatial investigations also show unique features of pavement condition deterioration attributed to diverse geometric characteristics and traffic conditions, such as vehicle compositions and volumes and urban and rural areas. The research findings are helpful to understand the characteristics of pavement distress conditions more clearly and to optimize traffic infrastructure design and maintenance.     

A systems-based approach to address unintended consequences of demand-driven transportation planning in national parks and public lands

In most cases, transportation planning in national parks and public lands might most appropriately be termed “demand-driven.” In this approach, rigorous analyses of park visitation, traffic, and parking data are used as a basis for transportation planning to accommodate current and projected future visitor demand, within financial constraints. Performance measures used to assess the quality of transportation systems in national parks are generally related to “moving people” efficiently. This approach is based on well-established principles for transportation planning in urban and rural communities. However, a demand-driven approach to transportation planning may not be suitable in national parks and public lands because it may enable levels of visitation that cause visitor crowding, resource impacts, and other unintended consequences. This paper introduces a more sustainable, systems-based transportation planning approach developed in the Rocky Mountain National Park (ROMO) to help the park operate its shuttle bus system efficiently and conveniently, and according to thresholds for visitor crowding and resource impacts at sites serviced by the shuttle system. The transportation planning approach developed in this study for ROMO is more suitable and sustainable for national parks and public lands than a demand-driven approach, and is readily adaptable to other locations. Correspondingly, the approach is now being applied in several other national parks and public lands recreation areas.     

The role of transportation accessibility in regional economic resilience

Whereas the importance of transportation for economic growth is widely acknowledged, past studies on the resilience of regions to economic shocks have not given explicit attention to the role of transportation accessibility on building robust regional economies. This exploratory study examines the regional performance in six U.S. states during the last recession (2008–2009) and post-recession (2010–2014) and evaluates its association with the transportation infrastructure. To account for spatial dependence and interactions, a exploratory spatial data analysis (ESDA), a global spatial autoregressive model, and a local Geographically Weighted Regression (GWR) are employed. Results show that, after controlling for other key aspects of resilience, such as industrial diversity and human capital, the global relationships between rail density, access to intermodal services, and access to local and regional markets were positively associated with regional performance (measured as competitive effect) during the recession. Similarly, positive regional performance before the recession period was associated with positive performance during the recession. The local spatial analysis, however, shows that the associations between the explanatory variables and regional performance vary significantly across space. The analysis and results of this study can contribute to a better understanding of the complex interactions between economic resilience and transportation infrastructure, and guide the development of policies and practices designed to strengthen the ability of regions to be resilient to economic shocks.     

Toward a spatial understanding of active transportation potential among a university population

Universities and surrounding communities stand to benefit when active travel mode choices are elevated. Despite this, there is little research on travel mode choice at commuter universities and, in particular, the nonlinear spatial relationships among active travel potential and various contextual and compositional factors. The purpose of this study was to examine and visualize linkages among personal, household, density, diversity, and design factors, and active travel (bicycling, walking, and mass-transit modes) among a commuter-university population residing throughout southeastern Michigan, USA. This was accomplished by employing exploratory spatial data analysis (ESDA), ordinary least squares (OLS) regression, and a geographically weighted regression (GWR) model. The GWR model outperformed the traditional OLS model in terms of goodness of fit (R² = .534 and R² = .461, respectively). A novel cartographic mapping technique was employed to depict where statistically significant parameter estimates negatively or positively influenced active travel. The main finding was that personal, household, density, diversity, and design estimates varied in both magnitude and spatiality throughout the university's study area. Interestingly, distance was not a universal barrier to active travel potential. These variations emphasize the importance of promoting active transportation through localized interventions as well as coordinating efforts among universities and surrounding communities.     

Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data

Traffic crashes are geographical events, and their spatial patterns are strongly linked to the regional characteristics of road network, sociodemography, and human activities. Different human activities may have different impacts on traffic exposures, traffic conflicts and speeds in different transportation geographic areas, and accordingly generate different traffic safety outcomes. Most previous researches have concentrated on exploring the impacts of various road network attributes and sociodemographic characteristics on crash occurrence. However, the spatial impacts of human activities on traffic crashes are unclear. To fill this gap, this study attempts to investigate how human activities contribute to the spatial pattern of the traffic crashes in urban areas by leveraging multi-source big data. Three kinds of big data sources are used to collect human activities from the New York City. Then, all the collected data are aggregated into regional level (ZIP Code Tabulation Areas). Geographically Weighted Poisson Regression (GWPR) method is applied to identify the relationship between various influencing factors and regional crash frequency. The results reveal that human activity variables from multi-source big data significantly affect the spatial pattern of traffic crashes, which may bring new insights for roadway safety analyses. Comparative analyses are further performed for comparing the GWPR models which consider human activity variables from different big data sources. The results of comparative analyses suggest that multiple big data sources could complement with each other in the coverage of spatial areas and user groups, thereby improving the performance of zone-level crash models and fully unveiling the spatial impacts of human activities on traffic crashes in urban areas. The results of this study could help transportation authorities better identify high-risky regions and develop proactive countermeasures to effectively reduce crashes in these regions.     

Determining intra-urban spatial accessibility disparities in multimodal public transport networks

Urban rail systems have been added to public transport systems, thereby changing distribution disparities in urban spatial accessibility. These disparities reflect both the ability of the public transport system to meet the needs of residents and the locational pros and cons of public service facilities. In this paper, integrated accessibility metrics are used to assess the disparities in Nanjing, Jiangsu Province, China. This is achieved by dividing the urban space into a multilevel grid that can be easily combined with grid-based population data to facilitate accessibility modeling, calculation, and evaluation. Additionally, an acquisition method for more accurate travel time data in the multimodal public transportation network was developed on the basis of an Internet mapping service. This provides a realistic, multimodal, door-to-door modeling approach that avoids the requirement of building complex traffic networks through Geographic Information System (GIS) software and simplifies road network modeling efforts. The results show that this modeling method can be used to reflect the accessibility disparities in the Nanjing urban space objectively and accurately.     

Built environment and potential job accessibility effects of road pricing: A spatial econometric perspective

This paper analyzes the impacts of the built environment (BE) as it relates to the potential job accessibility (PJA) effects of road pricing. The relationships between the BE elements and PJA under a road charging policy are established using a spatial econometric approach, which uses an integrated land use and transportation model (TRANUS model) and a spatial lag model (SLM). With the intent of further analyzing the differences in the PJA effects of road pricing on traffic analysis zones (TAZs) that contain different combinations of BE elements, a quantitative classification method combining factor and cluster analysis is applied. This will quantitatively categorize TAZs inside and outside the tolled areas. In exploring the relationship between changes in PJA and the road pricing policy, we found the spatial autocorrelation coefficient to be negative. This result suggests that we are unable to increase the PJA of all the regions through road pricing, but rather affect a redistribution of PJA between different regions. Results also indicate that the impacts of road charging on PJA are associated with urban BE elements. Moreover, such effects are the common result of specific characteristics of the BE. The higher the number of jobs, the better the public transportation conditions, and the better the street design (high densities of street and intersections), the less the region will be negatively influenced by a road charging policy, and vice versa. To avoid the negative effects of road pricing on PJA prior to the launch of such a policy, cities should improve public transportation networks and enhance the street design of the road pricing policy areas, especially the toll ring periphery area.     

Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen,China

Trip purpose is closely related to travel patterns and plays an important role in urban planning and transportation management. Recently, there has been a growing interest in investigating the spatio-temporal patterns of dockless shared-bike usage and its influencing mechanisms. Few, however, have focused on revealing the travel patterns by inferring the purpose of dockless shared-bike trips at the individual level. We present a framework for inferring the purpose of dockless shared-bike users, based on gravity model and Bayesian rules, and conduct it in Shenzhen, China. We consider the comprehensive factors including distance, time, environment, activity type proportion, and service capacity of points of interest (POIs), the last two factors of which were usually neglected in previous transport studies. Especially, we integrated areas of interest (AOIs) and Tencent User density (TUD) social media data characterize the service capacity of POIs, which reflect the area and scale differences of different POI categories. Through the comparison between two improved models and the basic model, it is demonstrated that the introduction of activity type proportion and service capacity of POIs can improve the effectiveness of model for inferring the purposes of dockless shared-bike trips. Based on the obtained trip purposes, we further explore the spatio-temporal patterns of different activities and gain some insights into bike travel demand, which can inform scientific decisions for bicycle infrastructure planning and dockless shared- bike management.     

Revealing intra-urban travel patterns and service ranges from taxi trajectories

As an important transport tool, taxi plays a significant role to meet travel demand in urban city. Understanding the travel patterns of taxis is important for addressing many urban sustainability challenges. Previous research has primarily focused on examining the statistical properties of taxi trips to characterize travel patterns, while it may be more appropriate to explore taxi service strategies on seasonal, weekly or daily time scale. Therefore, intra-urban taxi mobility is investigated by examining taxi trajectory data that were collected in Harbin, China, while 12-week corresponding to 12-month is chosen as the sampling period in our study. The multivariate spatial point pattern analysis is firstly adopted to characterize and model the spatial dependence, and infer significant positive spatial relationships between the picked up points (PUPs) and the dropped off points (DOPs). Secondly, the points of interest (POIs) are identified from DOPs using the emerging hot spot detection technique, then the taxi services and movement patterns surrounding POIs are further examined in details. Moreover, our study builds on and extends the existing work to examine the statistical regularities of trip distances, and we also validate and quantify the impacts posed by airport trips on the distance distributions. Finally, the movement-based kernel density estimation (MKDE) method is proposed to estimate taxis' service ranges within three isopleth levels (50, 75 and 95%) between summer/weekday and winter/weekend from taxi driver's perspective, and season as well as temperature factors are identified as the significant effect within certain service range levels. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.     

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai,China

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R² = 0.774) than the OLS regression model (Adjusted R² = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.     

Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap

Over the past two decades, smart card data have received increasing interest from transport researchers as a new source of data for travel behaviour investigation. Collected by smart card systems, smart card data surpass traditional travel survey data in providing more comprehensive spatial–temporal information about urban public transport-based (UPT) trips. However, the utility of smart card data has arguably yet to be exploited fully in terms of extracting and exploring the spatial–temporal dynamics of UPT passenger travel behaviour. To advance previous work in this area, this paper demonstrates a multi-step methodology in order to render more insightful spatial–temporal patterns of UPT passenger travel behaviour. Drawing on the Brisbane, Australia, bus network as the case study, a smart card dataset was first processed in combination with General Transit Specification Feed (GTFS) data to reconstruct travel trajectories of bus passengers at bus stop level of spatial granularity. By applying geographical information system-based (GIS) techniques, this dataset was used to create flow-comaps to visualise the aggregate flow patterns at a network level. The flow-comaps uncovered the major pathways of bus passengers and its variations over a one-day period. The differences within the flow-comaps were also quantified to produce weighted flow-comaps that highlighted the major temporal changes of passenger flow patterns along a number of stop-to-stop linkages of the bus network. The proposed methodology visually unveiled the spatial–temporal travel behaviour dynamics of UPT passengers and, in doing so, showed the potential to contribute to a new evidence base with the capacity to inform local public transport policy.     

Sustainability evaluation of rapid routes for buses with a network DEA model

This study establishes a network data envelopment analysis (DEA) model to evaluate the sustainability of public transportation services targeting rapid routes for buses in the Seoul metropolitan area. A network DEA-based optimization model is formulated to evaluate the sustainability of the public transportation service. By considering public transportation services from both the operators’ and users’ perspectives, this model produces results that reflect the interaction of three sustainable transport service properties, i.e., efficiency, equity, and environmental impacts. It is identified that the expansion of median bus lanes and the conversion of conventional buses into compressed natural gas vehicles could improve the sustainability of the public transportation services in the Seoul metropolitan area. Some limitations and future research agenda also are presented.