Distance-decay functions for daily travel-to-work flows

The non-homogeneity of geographic space brings about the processes that horizontally relate sections of geographic space, in transport geography referred to as spatial interactions. The distance separating different types of locations plays a crucial role in these interactions. Distance is the major factor that influences the values of interaction intensities. The question is how the intensities decrease with distance, since this decrease is usually not linear. This paper pursues the issues of the shape and parameters of the distance-decay functions based on daily travel-to-work transport movements, taking regional centres in the Czech Republic as the example. First the special distance-decay functions for individual regional centres are presented and discussed, followed by the expression of the universal distance-decay function approximating generally to the traits of the Czech settlement system and the nature of the interaction flows, i.e., travel-to-work. The expression of the universal function is based on the application of two easily accessible variables: population and number of jobs.     

A new approach to modelling distance-decay functions for accessibility assessment in transport studies

This paper tries to break new ground in how distance-decay relationships are modelled in accessibility and transport demand studies and does it based on an innovative approach to empirical data collection on psychological perceptions of distance in relation with activities located in space and a new aggregate distance-decay function. This new approach improves on the quality of the representation of spatial interaction effects on transport demand modelling studies that commonly rely on generic curves barely confronted with empirical data. We compare the level of fit of the proposed curve with other distance-decay functions mentioned in the literature and used in practice and draw relevant conclusions on the proper model specification.     

Using metro smart card data to model location choice of after-work activities: An application to Shanghai

A location choice model explains how travellers choose their trip destinations especially for those activities which are flexible in space and time. The model is usually estimated using travel survey data; however, little is known about how to use smart card data (SCD) for this purpose in a public transport network. Our study extracted trip information from SCD to model location choice of after-work activities. We newly defined the metrics of travel impedance in this case. Moreover, since socio-demographic information is missing in such anonymous data, we used observable proxy indicators, including commuting distance and the characteristics of one's home and workplace stations, to capture some interpersonal heterogeneity. Such heterogeneity is expected to distinguish the population and better explain the difference of their location choice behaviour. The approach was applied to metro travellers in the city of Shanghai, China. As a result, the model performs well in explaining the choices. Our new metrics of travel impedance to access an after-work activity result in a better model fit than the existing metrics and add additional interpretability to the results. Moreover, the proxy variables distinguishing the population seem to influence the choice behaviour and thus improve the model performance.     

Catchment if you can: The effect of station entrance and exit locations on accessibility

The success of passenger railway systems depends on their ridership and thus the population they serve. A mechanism to increase ridership is to expose the existing system to more people by reconfiguring the station itself, for instance by adding extra entrance and exit gates to shorten the walking distance from a trip's origin or its final destination. Gates are key nodes giving pedestrians access from street network to boarding/alighting facilities and vice versa. Stations and platforms are places not points, passengers may spend up to 6 min a trip walking between platforms and the end of the station nearest their origin or destination. This study systematically evaluates the accessibility of train stations and the effect of constructing an additional ‘far-side’ gate at stations with a single ‘near-side’ entrance. A three-step approach is defined to generate an isochrone as the catchment area for any transport node. Results indicate that stations with a single gate along their platforms (usually on one end of them) have the potential to increase the accessibility to jobs and population by around 10% on average. Due to the walking network and land use characteristics, some stations will benefit more significantly by retrofitting a new gate. Also, four linear regression models are developed to illustrate the effect of expanded accessibility on the number of entries and exits at each station for two peak periods. Then, stations are ranked based on their added ridership, which can help authorities to prioritize the development and allocating resources.     

Mode choice in commuting and the built environment in México City. Is there a chance for non-motorized travel?

Non-motorized transportation (NMT) has emerged as a mitigating alternative for the negative externalities of motorized travel. This research presents an overview of the intra-metropolitan geography of transportation mode choice in the journey to work in the México City Metropolitan Area (MCMA), which can be seen as a representation of the huge socioeconomic inequalities typical of the Global South. The regression model applied showed that, as expected, socioeconomic variables were strong determinants of mode choice. An increase in age, as well as lower categories of socioeconomic class and educational attainment, were associated with the use of transit and non-motorized travel. Other factors positively associated with bike use were distance to the center, density of mass-transit systems stations, street intersection density, and the flat surface. The pattern of walk commuting with respect to the city center followed a u-shaped curve, while factors significantly positively associated were female, population density, the jobs-housing ratio at the origin, and the density of mass-transit systems stations. The paper concludes with a discussion of the research implications to leverage public policy efforts to promote NMT.     

Enhanced Huff model for estimating Park and Ride (PnR) catchment areas in Perth,WA

A train station catchment area delineates the spatial territory from which the users of a train station are drawn. The size and shape of this catchment can be influenced by a variety of factors, such as the transport network, the location of stations and the service quality they offer, as well as the land use density and diversity in the transport corridor. Although numerous studies have been conducted to understand the size of catchment areas, limited research has focused on determining the spatial boundary (shape) of train station catchments. This paper develops a framework for deriving a spatial boundary of a Park and Ride (PnR) catchment area by incorporating the Huff model and Geographic Information Systems (GIS) technologies. The approach is staged, firstly determining the PnR station choice as a function of the attractiveness of a train station and the cost of access between the origin (such as a suburb) and the destination of a trip (such as the Perth CBD). Linear referencing method is then applied to re-define the origins to train stations based on the derived station choice probability. Finally, the spatial boundary of a catchment area is determined according to the adjusted origins, using GIS technologies. The model outputs were evaluated against licence plate survey of station users, where the Kappa coefficient (0.74) and overall accuracy (0.88) statistic suggested that the model's results are robust. The paper then shows how catchment area data can be used to better manage travel demand and plan design solutions aimed at increased accessibility to train stations.     

Neighbourhood effects on station-level transit use: Evidence from the Taipei metro

While large high-density metropolitan areas with extensive transit networks experience greater use of rail transit than elsewhere, less is known about the neighbourhood effects that affect station use. This study applies the 5D model to analyse neighbourhood effects within 600 m of transit stations in the Taipei metropolitan area. The area is separated into three concentric zones, with separate functions for each zone. While population density, destination attractiveness, and distance to intermodal connections are important in all three zones, design features depend on their location vis-à-vis the centre. Intersection density is important in the downtown core, while bike share facilities affect station use in the intermediate ring. A geographically weighted regression (GWR) reveal that most 5D variables exhibit spatial serial dependence. The key GWR result is that population density has the greatest effect on station use in peripheral residential neighbourhoods.     

Walking to transit: An unexpected source of physical activity

Using data from a regional survey carried out in Montreal, this paper shows empirical evidence that modal shift from car to transit contributes to the volume of daily physical activity. First, the paper presents a method to calculate the walking distance related to transit trips, and a totally disaggregate trip assignment model. The walking distance involved in every transit trip is then associated to the individuals' characteristics to estimate the number of steps made by the population in the whole transit system.Results show that, in average, a transit trip involves 1250 steps, required to access and egress the network as well as to transfer between routes or modes. Thus, a round trip represents 2500 steps, which account for 25% of the recommended volume of physical activity per day. Hence, analysis shows that the volume of walking varies according to attributes of the traveler (general decrease with age, higher for men) and of the trip (study and work trip involve more steps, the use of train generates more steps). A regression model confirms that these variables significantly affect the number of steps involved in transit trips. Such positive outcome, for the traveler himself, is an innovative argument to promote the use of transit that is also aligned with current sustainable transportation goals.     

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.     

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.     

Developing advanced route choice models for heavy goods vehicles using GPS data

This paper presents a novel application in route choice modelling using Global Positioning System (GPS) data, focussing on heavy goods vehicles which typically make longer journeys with decisions potentially underpinned by different priorities from those used by car drivers. The scope of the study is larger than many previous ones, using the entire road network of England. Making use of the error components model put forward for route choice by Frejinger and Bierlaire (2007), the work reveals low elasticities in response to changes in travel time, reflecting the limited opportunity for avoiding specific roads on long distance journeys by heavy goods vehicles.     

Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning

Although many studies investigate the association between land use and station ridership, few examine their nonlinear and moderating relationships. Using metro smartcard data in Shenzhen, we develop a gradient boosting decision trees model to estimate the relative importance of land use variables and their threshold and moderating effects on ridership. We found that station betweenness centrality has the largest predictive power, followed by employment density and commercial floor area ratio (FAR). Results suggest that employment density, commercial FAR, and aggregate residential density should be set at 40,000 jobs/km², 2, and 77,000 persons/km², respectively, for maximizing ridership. The moderating effects show that population densification is more effective at terminal stations, whereas the policies intensifying nonresidential use work better at middle stations. These findings help planners prioritize land use strategies, identify effective ranges of land use metrics, and propose land use guidelines adaptive to the network position of stations.     

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.     

Comparing the p-median and flow-refueling models for locating alternative-fuel stations

The p-median and flow-refueling models are two of the more popular models for optimal location of alternative-fuel stations. The p-median model, one of the most widely used location models of any kind, locates p facilities and allocates demand nodes to them to minimize total weighted distance traveled. In comparison, the flow-refueling location model (FRLM) is a path-based demand model that locates p stations to maximize the number of trips on their shortest paths that can be refueled. For a path to be considered refuelable, one or more stations must be located on the path in a way that allows the round trip to be completed without running out of fuel, given the vehicle driving range. In this paper, we analyze how well the facilities located by each model perform on the other’s objective function on road networks in Florida. While each objective function degrades somewhat when facilities are located by the other model, the stations located by the flow-refueling model generally do better on the p-median objective than the stations located by the p-median model do on the flow-refueling objective. This difference between the two models is even more pronounced at the state scale than at the metropolitan scale. In addition, the optimal locations for the FRLM tend to be more much more stable as p increases than those located by the p-median model.     

突发大客流在城市轨道交通网络中的传播机理

根据突发大客流在城市轨道交通车站的作业性质和特点,将城市轨道交通网络中的车站分为发生站、换乘站和中间站。在此基础上,通过站台候车区的乘客密度、到达列车的剩余载客能力、站台候车乘客数和乘客上车效率等因素,构建站台候车区乘客密度影响模型和列车运行延误时间模型,并总结出突发大客流在城市轨道交通网络中的传播机理。从车站服务水平和列车运行影响两个方面对突发大客流传播的影响因素及相应的调整措施进行分析。     

Transit ridership estimation with network Kriging: a case study of Second Avenue Subway,NYC

An attractive topic in transportation practice is transit ridership estimation. Reliable estimates are beneficial to spatial structuring, facility design, and vehicle operation, as well as financial and labor management. Traditional ridership estimation approaches mainly rely on regression models that consider subway fares, population, and employment distribution in surrounding areas. Yet consideration of ridership’s spatial dependency is largely lacking in these models. This paper recognizes the spatial effect by estimating the ridership of the new Second Avenue Subway in New York City using a network Kriging method. Network distance, instead of Euclidean distance, is used to reflect the fact that subway stations are only connected by subway tunnels. Results show that the new service should effectively relieve the traffic burden on other currently crowded subway lines.     

Latent class nested logit model for analyzing high-speed rail access mode choice

This paper explores access mode choice behavior, using a survey data collected in Taiwan. The latent class nested logit model is used to capture flexible substitution patterns among alternatives and preference heterogeneity across individuals while simultaneously identifying the number, sizes, and characteristics of market segments. The results indicate that a four-segment latent class nested logit model with individual characteristics in segment membership functions is the most preferred specification. Most high-speed rail travelers were cost-sensitive to access modes, and thus strategies that reduce the access costs can be more effective than reducing the access times.     

Optimizing EV-based P&R subsidy policies for commuting corridor based on cross-nested logit model

The promotion of electric vehicles (EVs) is restricted by cruising range limitation and charging station deficiency. Given the mature development of Park and Ride (P&R) mode, which is used in many cities worldwide to attract more travelers to use public transit, a new travel mode, i.e., EV-based P&R is introduced as an alternative for commuters’ daily travel. This seems quite attractive to expand the use of EVs and further increase their market share. This paper aims to investigate the impact of EV-based P&R introduction on travel mode choice along commuting corridor, and further aid in the optimal subsidy policies decision for the government. A bi-level model is proposed to model the presented problem. The lower level describes commuters’ joint mode and transfer choice behavior through a cross-nested logit (CNL) model, while the upper level minimizes the system cost. A genetic algorithm is developed to solve the formulated model with a partial linearization algorithm for solving the lower level model. And a numerical example is then used to demonstrate the effectiveness of the methodology and illustrate how the network flow pattern reshapes due to the introduction of EVs into the P&R mode and the change of corresponding subsidy policies. As the results show, improving the EV hardware, applying the intelligent supporting service system, developing new technologies for EV fast charging, appropriately improving the parking space capacity, and increasing the parking fee of transfer stations near the central business district (CBD) are all helpful to save the social cost and promote the usage of EVs.     

Predicting transit mode choice of New Jersey workers commuting to New York City from a stated preference survey

This study examines transit mode choice preferences of New Jersey commuters traveling to New York City (NYC) with the objective of assisting trans-Hudson ferry service planning on the basis of variables significantly affecting mode choice. A stated preference survey was conducted to collect data from a total of 2134 respondents who commute from northern New Jersey to NYC. A mixed multinomial logit model (MMLM) with random coefficients was used to analyze the data. The study considered the choice between four transit modes—ferry, PATH train, bus, and commuter rail—all of which can be used to cross the Hudson River to travel from New Jersey to NYC. Essential model results are presented for all four modes, but detailed results are discussed for only the ferry, PATH, and bus modes. Model results indicate that commuters' choices are affected not only by travel time and cost for the segment of the journey that crosses the Hudson River, but also reliability and comfort during that segment, number of access modes required by the journey, and travel time and cost of trips from home to stations/terminals before crossing the river. The results also show that commuters' attachment to the modes they currently use serves as a barrier to switching to other modes. The most important takeaway from this research for transit service planning is that there is a need to consider the attributes of both individual trip segments as well as entire journeys between commuters' home and work when exploring changes to transit service characteristics.     

Trip end models of local rail demand in England and Wales

This paper details models which have been developed to forecast the total number of trips made from local rail stations in England and Wales over a one year period. The use of multiple linear regression and geographically weighted regression in calibration are compared, with both explaining over 75% of the variation in the observed data. The latter technique has not previously been used in rail demand modelling, and allows significant spatial variations in the effect of independent variables to be identified and mapped. A number of catchment definition methods are investigated, as is the inclusion of a wide range of demographic and service related explanatory variables. The models developed are used to forecast usage at stations on the recently opened Ebbw Vale branch line in South Wales and these predictions are compared to initial usage figures.