Public bicycle as a feeder mode to rail transit in China: The role of gender,age, income,trip purpose,and bicycle theft experience

A marriage between public bicycle and rail transit presents new opportunities for sustainable transportation in Chinese cities. To examine determinants of public bicycle usage for rail transit access, an intercept survey of feeder mode choice among rail transit users was conducted near rail stations in Nanjing, China. Mode choice models were estimated with five feeder mode alternatives, including car, bus, walk, private bike, and public bike. By differentiating between public and private bicycle modes in the mode choice models, the study reveals the effects of personal demographics, trip characteristics, and station environments on public bicycle usage for rail transit access. Results show that female, older, and low-income rail commuters are less likely to use public bicycle to access rail transit. Rail commuters with bicycle theft experience and making school- or work-related trips are more likely to use public bicycle to access rail transit. Land use variables are largely insignificant in this study except that density shows a positive relationship with walking to rail transit. The results on demographic differences raise equity concerns when it comes to investing in public bicycle systems. Policy implications are discussed for Chinese cities to equitably boost public bicycle integration with rail transit.     

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.     

Design of a rail transit line for profit maximization in a linear transportation corridor

This paper addresses the design problem of a rail transit line located in a linear urban transportation corridor. The service variables designed are a combination of rail line length, number and locations of stations, headway and fare. Two profit maximization models, which account for the effects of different transit pricing structures (flat and distance-based fare regimes), are proposed. In the proposed models, the effects of passenger demand elasticity and population density along the urban corridor are explicitly considered. The solution properties of the proposed models are explored and compared analytically, and the indifference condition for the two fare regimes in terms of the operator’s net profit is identified. A heuristic solution algorithm to solve the proposed models is presented. Numerical examples are provided to show the effects of the fare regimes, rail capital cost and urban configuration (in terms of urban population distribution and corridor length) on the design of the rail transit line and the profitability of the rail transit operations.     

Transit ridership forecasting at station level: an approach based on distance-decay weighted regression

This article develops a rapid response ridership forecast model, based on the combined use of Geographic Information Systems (GIS), distance-decay functions and multiple regression models. The number of passengers boarding at each station in the Madrid Metro network is estimated as a function of the characteristics of the stations (type, number of lines, accessibility within the network, etc.) and of the areas they serve (population and employment characteristics, land-use mix, street density, presence of feeder modes, etc.). The paper considers the need to evaluate the distance threshold used (not the choice of a fixed distance threshold by assimilation from other studies), the distance calculation procedure (network distance versus straight-line distance) and, above all, the use of distance-decay weighted regression (so that the data from the bands nearer the stations have a greater weighting in the model than those farther away). Analyses carried out show that weighting the variables according to the distance-decay functions provides systematically better results. The choice of distance threshold also significantly improves outcomes. When an all-or-nothing function is used, the way the service area is calculated (straight-line or network distances) does not seem to have a decisive influence on the results. However, it seems to be more influential when distance-decay weighting is used.     

Integrating node-place and trip end models to explore drivers of rail ridership in Flanders,Belgium

The node-place model is an analytical framework that was devised to identify spatial development opportunities for railway stations and their surroundings at the regional scale. Today, the model is predominantly invoked and applied in the context of ‘transit-oriented development’ planning debates. As a corollary, these model applications share the pursuit of supporting a transition towards increased rail ridership (and walking and cycling), and therefore assumingly a transition to more sustainable travel behavior. Surprisingly, analyses of the importance of node and place interventions in explaining rail ridership remain thin on the ground. Against this backdrop, this paper aims to integrate the node-place model approach with current insights that derive from the trip end modeling literature. To this end, we apply a series of regression analyses in order to appraise the most important explanatory factors that impact rail ridership in Flanders, Belgium, today. This appraisal is based on both geographical and temporal data segmentations, in order to test for different types of railway stations and for different periods of the day. Additionally, we explore spatial nonstationarity by calibrating geographically weighted regression models, and this for different time windows. The models developed should allow policy and planning professionals to investigate the possible demand impacts of changes to existing stations and the walkable area surrounding them.     

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.     

The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S.

Vehicle ownership is an important determinant of the travel demand forecasting process. Vehicle ownership models are used by policy makers to identify factors that affect vehicle miles traveled, and therefore address problems related to energy consumption, air pollution, and traffic congestion. For the conventional travel demand forecasting, it logically follows land use forecasting, before trip generation, which is commonly treated as step one. The most critical limitation of the vehicle ownership models, especially in the conventional process, is that they are often related mainly to sociodemographic variables, not so much to built environmental variables. In this study, by pooling regional household travel survey data from 32 diverse regions (almost 92,000 households) of the U.S., and by controlling for socio-demographic and the built environmental variables, we estimated a vehicle ownership model that contributes to the understanding of vehicle ownership and improves the accuracy of travel demand forecasts. Two main findings of this research are: 1) The number of vehicles owned by a household increases with socio-demographic variables and decreases with almost all of the built environmental variables. For the urban planning and design practices, this finding suggests that car shedding occurs as built environments become more dense, mixed, connected, and transit-served. 2) We used both count regression and discrete choice models, and the results suggest that count regression models have better predictive accuracy. The model developed in this study can be directly used for travel demand modeling and forecasting by metropolitan planning organizations.     

The value and effects of introducing high standard train and bus concepts in Blekinge, Sweden

A set of improvements were introduced on a railway line in the south of Sweden in January 1992, including a new train concept, improved timetable and lower fares. The new train replaced a combination of old rail cars and standard coaches (bus). Furthermore, 18 months later, a new high comfort coach (bus) service was introduced as a complement to the train timetable to increase the number of departures. Forecasts from a national four-step model system, based on Revealed Preference data, which did not cover explanatory factors like mode concept, comfort, mode image, was made and compared with a forecast based on stated preference data choice model. Both type of forecasts were compared to travel statistics. Stated Preference data have been collected from car drivers, coach and train passengers. The set of improvements have been included in the SP alternatives presented to the subjects. A structured logit model, a regression model and a linear programming model are compared. Unstructured and structured logit models are compared. Logit models have been estimated and used to form a mode choice model which predicts the changes in market shares between the train and car. The choice model forecast coincide, accidentally, with actual demand 8–9 months after the introduction of the set of improvements (a 40% increase in journeys). The set of improvements, including the new train service, has proven to be a success with more than 100% increase in travel after only two years of operation. Differences in estimated logit models for 1991 and 1992 are explained as partly due to asymmetry effects and partly to shortcomings in the SP data collection technique. Logit model estimates for train and coach are compared. The value of high standard coach service, running in parallel to the train, is estimated. The new coach gained few new passengers and also a much lower monetary value than the train by both train and bus passengers.     

Urban rail systems investments: an analysis of the impacts on property values and residents’ location

Light rail, metro and other urban rail transit systems can play a significant role in improving the attractiveness and quality of urban public transport. They can influence the attractiveness of locations near the stations and improve accessibility for these locations. Furthermore urban rail can improve a location’s attractiveness by its image effect: it makes a station appear modern and dynamic, and thus raises the status of this location.This paper summarises findings on the land-use and economic impacts of the urban rail system of the city of Naples over time and space. It examines changes in residential and non-residential (offices and retail) property prices around the newly built stations between 2001 and 2008 as well as the changes in the number of residents for the same station catchment areas. Ad hoc station control areas have been specified in order to compare the results of these changes. Results show that values in station control areas are lower than those of those of the stations catchment areas.     

Consumer benefits and demand impacts of regular train timetables

This paper reports novel research into the benefits that rail travellers receive from more regular features of timetables over and above any benefits of improved frequency. A stated preference (SP) exercise amongst rail travellers was conducted to estimate these benefits and the generally plausible results have been used to enhance a rail demand model which in turn has been used to forecast the effect on demand of more regular timetables for a range of situations. Not surprisingly, the demand impacts are generally relatively small, although they would be welcome additional benefits in the evaluation of a regular timetable.     

Modelling the joint access mode and railway station choice

This study models the choices of Dutch railway users. We find a steeper negative distance effect on the utility of departure stations accessed by the non-motorized modes of walking and bicycle as compared to the motorized modes of car and public transport. Availability of parking places and bicycle standing areas have a positive effect on the choice of departure railway stations accessed by car and bicycle, respectively. Public transport frequency has a positive whereas travel time has a negative effect on the choice of departure stations accessed by public transport. The derived rail service quality index (RSQI), which provides a measure of rail accessibility to all other stations, has a significant and positive effect on the choice of departure stations accessed by all modes. The outcome of this paper can be used to develop a comprehensive railway accessibility indicator for neighbourhoods, for hedonic pricing studies.     

Residential property value impacts of proximity to transport infrastructure: An investigation of bus rapid transit and heavy rail networks in Brisbane,Australia

Public transport investment is normally targeted at increasing accessibility which land rent theory identifies and will in turn increase land values. There is a clear policy interest in how much land values increase following a new transport investment so as to establish if there is sufficient land value uplift to capture and to help pay or contribute to investment plans. Identifying an uplift for residential land has been well studied in the context of new light rail systems and bus rapid transit (BRT) systems in developing countries but there is little evidence for BRT in developed countries.This paper has two objectives. First, to examine long term impact of BRT in a developed world context in Brisbane, Australia. Brisbane's BRT uses an open system design which contrasts with the closed system design of the successful BRT systems in South America and elsewhere, including the BRT in the suburbs of Sydney, Australia. Second, BRT in Brisbane was introduced to a network already dominated by a radial heavy rail network and this investigation recognises that the uplift from BRT introduction may therefore be different to a BRT in a single mode city. A third motivation is to consider the spatial distribution of uplift which is an essential pre-requisite to understanding the distributional impact if uplift is used to contribute to infrastructure provision.Spatial modelling is used to examine the accessibility impacts of the BRT at a global level. This is followed by Geographical Weighted Regression, used to examine the spatial distribution of accessibility using a local model.The results show that there is greater uplift in Brisbane, as compared to that identified by studies of Sydney's BRT which is likely due to the greater network coverage of BRT in Brisbane and less strong competition of rail. Land value uplift is also spatially distributed over the network giving higher uplift in some areas than others and lower values than typically found with rail based systems in developed countries. However, the degree of uplift is relatively low, with proximity to BRT stations attracting more uplift than proximity to train stations.     

Modeling AADT on local functionally classified roads using land use,road density,and nearest nonlocal road data

The focus of this research is to model the influence of road, socioeconomic, and land-use characteristics on local road annual average daily traffic (AADT) and assess the model's predictability in non-covered location AADT estimation. Traditional ordinary least square (OLS) regression and geographically weighted regression (GWR) methods were explored to estimate AADT on local roads. Ten spatially distributed counties were considered for county-level analysis and modeling. The results indicate that road density, AADT at the nearest nonlocal road, and land use variables have a significant influence on local road AADT. The GWR model is found to be better at estimating the AADT than the OLS regression model. The developed county-level models were used for estimating AADT at non-covered locations in each county. The methodology, findings, and the AADT estimates at non-covered locations can be used to plan, design, build, and maintain the local roads in addition to meeting reporting requirements. The prediction error is found to be higher at urban areas and in counties with a smaller number of local road traffic count stations. Recommendations are made to account for influencing factors and enhance the local road count-based AADT sampling methodology.     

A methodology for prioritising HSR corridors: from U.S. theory to Spanish practice

The assessment of methodologies to prioritise the construction of new high-speed rail (HSR) corridors has recently become a key issue for transport planners in countries like the U.S., where HSR does not exist. In a climate of financial instability since the onset of the global crisis of 2007–2008, support for new projects is being eroded by serious concerns over the extremely steep costs of high-speed rail, and there is a need for the scientific clarification of the procedure for prioritizing construction.In 2009 a new ranking methodology was developed and applied to 30,000 city pairs in the U.S. to determine their suitability for high-speed rail investment. The existing literature on ranking tools for prioritising HSR corridors is practically non-existent, and, as none of these U.S. lines has been built or put into in operation, this methodology has not been validated. The main aim of this paper is to contribute to these ranking models and highlight their importance in the HSR planning process. The preliminary ranking tool described in this work has been validated using data from the current HSR Spanish network. The results confirm the consistency of the model as a first approach to ranking pairs, mainly for the top O–D relations; however the model has some drawbacks chiefly due to the type of variables used, and their assigned weightings. Finally, some specific improvements are proposed for this ranking approach in order to provide policymakers with a useful tool when planning the construction of new HSR networks.     

Understanding the roles of rail stations: Insights from network approaches in the London metropolitan area

In strategic transport planning, rail stations play different roles at different scales, particularly in large cities and metropolitan regions, where movement structure shows polycentric patterns. However, there is a general lack of evidence-based studies investigating the relative network roles of rail stations. Therefore, this paper aims to explore rail stations' roles within and beyond metropolitan communities in London. Drawing on the Transport for London's newly released NUMBAT passenger origin-destination data, we unravel the travel structure and identify the stations' roles through network methods, community detection and Z-P scores. Our research has three results: First, London's passenger movement network can be subdivided into seven communities. The majority of stations within the same communities are geographically localised with few exceptions. Second, stations play distinct roles in handling the patterns of movement at different scales. Typically, there exists a set of stations that are significant for connecting between metropolitan communities, because the disruption of those stations will have a greater impact on overall network efficiency and affect a larger and more diverse group of passengers. Third, a classification of rail stations is developed based on stations' commitment at two scales. This classification allows planners to have a finer level of understanding of the network role of the stations and thus develop and adjust tailored (re)development strategies.     

The potential of rotorcraft for intercity passenger transport

There are many high-speed rail projects in Europe at different stages of development. They are supported on environmental grounds and as a means of reducing congestion on other modes, including aviation. High-speed rail, however requires major investment costs, takes a long time to come online, and involves considerable financial risks. A potential alternative that overcomes some of these problems makes use of rotorcrafts. This paper assesses the potentiality of civil rotorcrafts as part of the air transport system. Discrete mode choice models are used to forecast traveller choice behaviour when the rotorcraft is introduced and financial evaluations performed looking at the operating costs of the competing transport modes.     

Driving demand: What can gasoline refueling patterns tell us about planning an alternative fuel network?

Knowing which variables predict gasoline demand can help inform which are useful in determining future demand at an alternative fuel station such as those for bio-fuels, natural gas, hydrogen, or fast-charge electricity. This study explores the spatial distribution of demand by comparing two main classes of variables: those without a displacement component such as population in a census block group, and those that imply a vector or directionality such as vehicle kilometers traveled. The spatial distribution of these variables is compared to the spatial distribution of demand for gasoline using regression. Many models examining the transition from gasoline to an alternative fuel assume a demand pattern for fuel a priori in order to estimate potential demand at a future alternative fuel station. This paper studies not the models themselves but the variables used to predict demand. The results indicate that vehicle kilometers traveled (VKT) is the best variable to pinpoint where demand for fuel will occur. However, travel to the central business district of the metropolitan area does not appear to translate into demand for fuel in proportion to the VKT. While gasoline demand does appear to vary with population as well, the location of demand is much less specific than that predicted by VKT. The results also suggest that the route between home and the nearest freeway entrance may help predict a large portion of refueling and merits further investigation. This possible tendency can be used to create a new variable called “population-traffic” which appears to describe the spatial distribution of demand well. The good performance of this independent variable in regressions suggests that stations sited along the freeway may serve customers needs and provide the necessary concentration of demand for initial alternative fuel stations. A practical application of this work would be to help define refueling demand patterns in a rollout of alternative fueled vehicles in a neighborhood or town.     

Time series forecasting of quarterly railroad grain carloadings

The participants in the grain logistics system need forecasts of railroad grain carloads. Although forecasting studies have been conducted for virtually every mode, no forecasting studies of quarterly railroad grain transportation have been published. The objectives of the paper are (1) specify a US quarterly railroad grain transportation forecasting model, and (2) empirically estimate the model. The selection of explanatory variables requires that they have a theoretical relationship to railroad grain transportation supply and/or demand, and that the data for the explanatory variables are published in quarterly frequency. However, there are relatively few potential explanatory variables that are published quarterly and those that are available appear to have weak correlation with quarterly railroad grain carloadings. The economic process generating quarterly railroad grain carloadings is quite complex and very difficult to model with regression techniques. Given this problem and the focus on short run forecasting, a time series model was employed to forecast quarterly railroad grain carloadings. An AR(4) model was estimated using the Maximum Likelihood estimation procedure for the 1987:4–1997:4 period. The actual railroad grain carloadings for this period were compared to the forecast carloadings generated by the time series model. For 92% of the 37 quarters the percentage difference between the actual and forecast values was 10% or less. Of the 9 annual observations, the per cent difference between the actual and forecast value was less than 2.6% for 8 of the 9 years. ©     

Developing new urban public transport systems: An irrational decision-making process

The decision-making process for planning new urban public transport systems in the UK is examined by studying 11 new and planned systems. The objectives of building the systems relate to transport and development issues. A number of key factors are taken into account during the decision-making process: forecast demand, image, deregulation of buses, technological innovation, private sector involvement and the funding mechanism. The requirement for proven technology and the forecast demand for the systems reduces the choice to one of light rail or bus. The effects of bus deregulation, funding and image, essentially reduce that choice to light rail or nothing. Cost should be kept low, which suggests a bus based system, but current legislation favours more expensive light rail systems. The conclusion is that although transport planners make rational decisions within the current political framework, the framework, and therefore some of the decisions, are not rational.     

Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption

This research analyzes the relationship between bike-sharing and public transit using bike-sharing data collected in Cologne, Germany. The selected system is one of very few in Germany that is organized as a free-floating system, which allows the generation of more detailed data. A construction site in the light rail network causing multiple disruptions in the public transit network offered the possibility to detect changes in bike-sharing usage that occur in the corresponding period. Applying negative binomial regression, spatial and temporal usage patterns are analyzed to identify connections to the public transit network and other factors influencing the usage of bike sharing. The analysis suggests the existence of a spatial relationship between bike-sharing and public transit. Therefore, an intermodal use of both means of transport can be assumed. The short-term changes in the public transit network caused by the construction site only have minor impacts on the usage patterns. Other factors that affect the usage structures could be identified. Proximity to universities as well as the number of certain points of interest nearby, such as food outlets and shops, promote bike-sharing use. Higher temperatures are also positively correlated, while rain reduces usage. The findings of the study can be beneficial to integrate bike-sharing into urban transport systems, especially regarding public transit.