Factors associated with disability paratransit's travel time reliability

This paper identifies some of the characteristics of trips and pick-up and drop-off locations that are associated with paratransit's travel time reliability. Following convention, reliability has been defined as the inverse of variability. Four measures of travel time variability have been used to examine reliability: Standard Deviation, Percent Variation, Misery Index, and Buffer Index. Regression models have been used to estimate these four variables with trip data from Access Link, the paratransit service provided by NJ TRANSIT pursuant to the Americans with Disabilities Act (ADA). A number of characteristics of the pick-up and drop-off locations as well as selected characteristics of the trips were used as independent variables of the models. The statistical significance of the independent variables varied depending on which measure of reliability was estimated, but a few variables were consistently associated with reliability in all four models. These variables were trip distance, booking type, winter season, density of motor vehicle crashes in pick-up and drop-off locations, and whether pick-ups occurred in suburban bus corridors or urban core areas. Because of the significance of the variables on motor vehicle crash density in pick-up and drop-off locations, an additional regression model was used to examine the effect of crash incidents on trip duration by considering drop-offs that occurred in locations immediately after a crash. The model showed that trips take 4 to 5% longer when crashes occur in locations prior to a drop off. Planning implications of the findings are discussed.     

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.     

Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity

Traffic state in the urban network is a direct reflection of the operational efficiency of the urban transportation system. As the busiest period of the day, traffic states during evening peak hours can effectively measure the capacity and efficiency of the transportation system. The primary objective of this study is to investigate how the potential factors affect traffic states during evening peak hours on weekdays. The geographically weighted regression (GWR) approach was proposed to model the spatial heterogeneity of traffic states and visualize the spatial distributions of parameter estimations. Four types of data including traffic state index (TSI) data, point of interests (POIs) data, road features data, and public transport facilities data were obtained from Shanghai in China to illustrate the procedure. According to the results, the GWR model outperformed the ordinary least square (OLS) model in the explanatory accuracy as well as the goodness of fit. The urban form was revealed to have a significant influence on traffic states and strong local variability for parameter estimations was observed. The number of public and commercial POIs, residential POIs, bus routes, bus stops, the average number of lanes, as well as average traffic volumes can significantly affect the traffic states spatially, and the estimated coefficients of each traffic analysis zone (TAZ) vary across regions. The conclusions of this study may contribute to making the planning and management strategies more efficient for alleviating traffic congestion.     

Urban spatial structure,employment subcenters,and freight travel

Metropolitan areas in the U.S. have become increasingly polycentric. Large employment subcenters have emerged outside of central cities, competing against the traditional city center for labor and businesses. The existing literature on land use and transportation focuses on passenger travel, providing little insight into the impact of polycentric metropolitan development patterns on freight activity. In this study, we use the Los Angeles region as a case study to examine the relationship between urban spatial development patterns and freight travel. Using the National Employment Time Series (NETS) data, we identify employment subcenters in metropolitan Los Angeles. We characterize freight activities associated with the subcenters using data from the Southern California Association of Governments (SCAG). We develop a regression model that estimates freight activity as a function of geographic characteristics, such as whether a location is in an employment subcenter, measures of nearby employment, access to the highway network, and proximity to intermodal freight facilities. The results indicate that employment is an important driver of freight activity; however, employment subcenters have an independent effect on freight activity. The results of this study suggest that further research on urban spatial structure and freight activity should assess the effects of employment subcenters and how their particular employment composition and characteristics are associated with freight activities at the metropolitan level. Such an approach would lead to more precise policy recommendations for urban goods movement.     

Assessing the role of the built environment and sociodemographic characteristics on walking travel distances in Bogotá

Planning for more sustainable mobility is an important goal for public authorities worldwide. Setting better conditions for pedestrians and cyclists has been related to many social benefits, such as less pollution and congestion. Local authorities, aware of the relationship between transport and land-use, are adopting planning tools to analyze growth in cities and their impacts using multiple indicators. This paper develops an econometric trip length model that correlates socioeconomic characteristics and built environment conditions with walking travel distances in the city of Bogotá, Colombia. A log-linear regression model was estimated using information for 2015 of Bogotá's household travel survey, along with data of employment and population by block obtained from official sources. The built environment component of the model serves to evaluate the effect of land-use patterns. A proof of concept was developed using land-use information extracted from a Cellular Automata Land-Use model calibrated for the city. From its results, some factors of the built environment were estimated and two scenarios were tested in this study. One depicts the average walking distance for households surveyed in 2015, and the other takes a simulation of land-use policy in developing a new urban area as an input. The results indicate that people living in areas were the land-use mix and job/population ratio are low, tend to have longer walking distances, suggesting the need to implement special policies that reallocate some activities and encourage higher land-use mix in the urban periphery where low-income households tend to be located. The proposed method can identify the impact of land-use policies in the distances traveled by walking, facilitating the impact assessment of land-use regulations.     

Assessment of the socio-spatial effects of urban transport investment using Google Maps API

We analyze the spatially distributed impacts of transport investment in urban highways and public transport with a novel methodology based on the capabilities of online technology to replicate the (unobserved) condition without highways. This is based upon the intensive use of Google Maps API (GMA) to obtain travel times between each origin-destination pair at a highly detailed level to reveal the effects of new infrastructure on different zones and groups within a city. Santiago is used as a case study, as the city introduced 150 km of urban highways, a reorganization of surface transit, and new subway lines in a relatively short period. We show that the high-income segment of the population has been the most favored, simultaneously increasing the difference between transit and car travel times in those areas where car ownership is low, stimulating the acquisition of a car.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

Influences of rural built environment on travel mode choice of rural residents: The case of rural Sichuan

China has entered a stage in which new rural construction and urbanization are rapidly developing. Considerable changes are occurring in rural China, and the built environment is different from that in the past; such difference directly influences the travel mode choice of rural residents. However, our knowledge on how the rural built environment influences the travel mode choice of rural residents in China remains limited. To fill this gap, this study combines on-site measurement methods, geographic information system (GIS) technology, and activity diary survey to obtain basic data regarding the built environment and the daily activities of rural residents. The multinomial logit (MNL) model is used to explore the relationship between the rural built environment and the travel mode choice of rural residents. Results show that building density significantly positively affects private car trips. This finding challenges earlier urban built environment research due to the considerable gap between rural and urban areas. An increase in road density increases the travel frequency of electric bicycles and motorcycles. Accessibility perception and preferences positively affect the probability of choosing to walk. Safety and neighborhood harmony perception positively affect the travel frequency of motorcycles and private cars. Rural residents who prefer a safe living environment are likely to choose walking for their daily travel. Despite the considerable achievements in the construction of rural roads, the frequency of public transportation remains low for rural residents. Therefore, additional attention should be given to the investment and construction of public transport facilities during rural urbanization.     

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.     

Ride-sourcing compared to its public-transit alternative using big trip data

Ride-sourcing risks increasing GHG emissions by replacing public transit (PT) for some trips therefore, understanding the relation of ride-sourcing to PT in urban mobility is crucial. This study explores the competition between ride-sourcing and PT through the lens of big data analysis. This research uses 4.3 million ride-sourcing trip records collected from Chengdu, China over a month, dividing these into two categories, transit-competing (48.2%) and non-transit-competing (51.8%). Here, a ride-sourcing trip is labelled transit-competing if and only if it occurs during the day and there is a PT alternative such that the walking distance associated with it is less than 800 m for access and egress alike. We construct a glass-box model to characterise the two ride-sourcing trip categories based on trip attributes and the built environment from the enriched trip data. This study provides a good overview of not only the main factors affecting the relationship between ride-sourcing and PT, but also the interactions between those factors. The built environment, as characterised by points of interest (POIs) and transit-stop density, is the most important aspect followed by travel time, number of transfers, weather, and a series of interactions between them. Competition is more likely to arise if: (1) the travel time by ride-sourcing <15 min or the travel time by PT is disproportionately longer than ride-sourcing; (2) the PT alternative requires multiple transfers, especially for the trips happening within the transition area between the central city and the outskirts; (3) the weather is good; (4) land use is high-density and high-diversity; (5) transit access is good, especially for the areas featuring a large number of business and much real estate. Based on the main findings, we discuss a few recommendations for transport planning and policymaking.     

Travel and activity time allocation: An empirical comparison between eight cities in Europe

A study of daily time allocation to travel and out-of-home activity is conducted across eight European cities over three countries: France (Lyon, Grenoble, Strasbourg and Rennes), Switzerland (Geneva, Bern and Zurich) and Belgium (Brussels), based on individual travel survey data collected between 1997 and 2006. The effects of socio-demographic, spatial context, transport availability and city-specific variables are investigated thanks to the Cox proportional hazard model. The results indicate that socio-demographic characteristics and city (or country) specific effect play a major role while residential density and proximity to high level road or public transport networks have a very limited impact on time budgets for travel and out-of-home activities.     

Online and off‐line travel packages preferences: a conjoint analysis

Past research has suggested that a number of travel package attributes impact on people's choices. In the present study the impacts of a number of these elements (price, package characteristics, travel agents and a seal of approval) in online and off‐line environments were examined using conjoint analysis. It was found that price had the biggest impact, although travel agent and airline reputation and trustworthiness also impacted on people's preferences. Interestingly, there were no significant differences in the attributes' impacts in the online and off‐line environments. Copyright © 2008 John Wiley & Sons, Ltd.     

An exploration of the environmental and rider characteristics associated with disability paratransit trip delay

Over the past two decades, a number of studies have been published on the efficiency of paratransit service for persons with disabilities. Although speed, delay, and pick-up duration can significantly affect overall efficiency of paratransit service, studies that have focused on these performance measures have been rare. This study examines how these performance measures are associated with local environmental characteristics such as density and the characteristics of the trip makers. It uses a dataset containing detailed information on a large volume of trips made by the registered clients of Access Link, a paratransit service operated by NJ TRANSIT for persons with disabilities. To measure speed and delay, network distances for 1.91 million trips were estimated by the ArcGIS Network Analyst extension. Analysis of variance and regression models were used to examine the associations between the performance measures and a set of variables pertaining to trips, passengers, and characteristics of pick-up and drop-off locations. Models for the entire study area as well as specific Access Link regions show that there is a significant association between local environmental and personal characteristics of passengers and the performance measures. Evidence was found that higher density of population, employment, and intersections at the local level may have a significant adverse effect on service efficiency because of lower speed and higher delay. Planning implications of the findings are discussed.     

Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice

The study investigates the role of the built environment attributes and their contextual effects on travel behaviour. The study utilized a dataset of 4739 respondents elicited from an online survey distributed in Hamilton City, Canada. A Nested Logit (NL) model and a quadratic polynomial trend surface are employed to spatially investigate the determinants influencing mode choice behaviour. The study contributes to our understanding of how geography moderates the impact of built environment attributes on mode choice behaviour. Socioeconomic demographics are found to play a pivotal role in explaining Hamiltonians' mode choice behaviour. For built environment attributes, sidewalk density is positively associated with walking and public transit use. Moreover, bike lane density is positively associated with biking and negatively associated with public transit use. Regarding land-use entropy (mix), the results show that high land-use entropy is negatively associated with choosing the car as a passenger travel mode. From a contextual perspective, the results affirmed that the influence of built environment attributes is not equally efficacious across the city. Improving the built environment attributes across the city reveals a substantial increase in walking and biking while decreasing the probability of choosing other modes. However, it is noteworthy to mention that the influence of improving the built environment is not homogeneous over geography.     

Built environment effects on leisure travel for children: Trip generation and travel mode

This study empirically analyzed the effects of built environment on leisure travel among children. Students of three elementary schools, namely Yangmingshan, Sanyu and Shilin, all located in the Shilin District of Taipei, were chosen to provide sample data. The negative binomial regression model and multinomial logit model were used to analyze trip generation and travel mode, respectively. This study reached the following empirical findings: (1) mixed land use, employment density, walkway quality, leisure facility supply and leisure travel distance encouraged generation of leisure trips for children; (2) intersection density, building density, employment density and walkway quality encouraged a child to use transit systems or non-motorized travel modes for leisure travel; and (3) vehicle density and leisure travel distance discouraged walking and biking but encouraged the use of transit systems for leisure travel involving children. Local government can use the empirical findings of this study to develop urban planning strategies to encourage children to perform leisure activities outside the home using transit systems or non-motorized travel modes.     

Transportation conditions and access to services in a context of urban sprawl and deregulation. The case of Dar es Salaam

Major deficiencies in urbanisation and transportation systems are reinforcing patterns of social and urban segregation in Dar es Salaam, Tanzania's largest city. Analysis of the 1993 Human Resources Development Survey shows that there are numerous obstacles to the daily travel of the city's inhabitants, notably the poor. These barriers weigh heavily on schedules, complicate access to services ever further, limit the use of urban space, and place considerable pressure on household budgets. Consequently, the poorest individuals tend to retreat into their neighbourhood where the low-quality urban facilities are unable to assist in the development of human and social capital and economic opportunities, the alleviation of poverty or the prevention of social exclusion.     

Solving a static repositioning problem in bike-sharing systems using iterated tabu search

In this paper, we study the static bike repositioning problem where the problem consists of selecting a subset of stations to visit, sequencing them, and determining the pick-up/drop-off quantities (associated with each of the visited stations) under the various operational constraints. The objective is to minimize the total penalties incurred at all the stations. We present an iterated tabu search heuristic to solve the described problem. Experimental results show that this simple heuristic can generate high quality solutions using small computing times.     

Access to the city: Mobility patterns,transport and accessibility in peripheral settlements of Dar es Salaam

This paper is concerned with access to the city for urban residents living in the periphery of Dar es Salaam, Tanzania. The paper presents an analysis of the mobility practices of residents and investigates the mobility constraints they experience in relation to the limited accessibility provided by the urban transport system. The paper draws upon qualitative interviews with residents in the periphery as well as recently collected travel speed data and offers a unique combination of testimony with GIS-based modelling of overall accessibility. A central finding is the overall importance of regular mobility and access to the city for residents in the periphery. Regular mobility is an ingrained part of residents' livelihood strategies. The majority of households rely on one or more members regularly travelling to central parts of the city in relation to their livelihood activities. The analysis reveals a widespread, near-to-universal, dependence on motorized transportation, with the vast majority depending on public transport. Raster-based modelling of overall accessibility provides an indication of the very high travel times endured by residents in the periphery. The analysis identifies and distinguishes between three overall mode types: 1) Private car, 2) public transport and 3) motorcycle/car combined with public transport. While private cars appear marginally faster, differences in travel times are actually limited. This suggests that travel times are less influenced by mode of transport than by road and traffic conditions and highlights how accessibility problems of peripheral settlements are not easily understood separately from the general dysfunctions of the overall mobility system of city.