A consolidated model of trip distribution

This work analyzes and compares various trip distribution models with spatial aggregation within a common theoretical framework for formulating and solving multi-objective optimization problems. A new model is designed that incorporates the main characteristics of existing ones. These models are then calibrated with a single database at different spatial aggregation levels using maximum likelihood. The results show that with aggregated data the various models differ little, but with disaggregated data the differences are considerable. It is also demonstrated that changing the level of data aggregation can significantly alter the models’ parameter values.     

Influence of varied parking tariffs on parking occupancy levels by trip purpose

The potential impact of parking-pricing on trip generation and modal choice is gaining greater acknowledgement within transport demand management research. However, although the aggregate effect of a transport demand management pricing measure is often noted or estimated, the potential varied impact of pricing measures on specific subsets of the market are often overlooked in the policy process. The variance of price impacts on different trip purposes, initially, and as tariffs increase progressively, is an important consideration for policy makers. Using the results from a survey on 1007 on-street parkers in Dublin, Ireland, this paper shows a progressively widening gap in price sensitivity between trips made for business purposes relative to non-business purposes, as the suggested parking pricing scenarios are increased. The results highlight the complication that the varied price sensitivity of particular market subsets can bring to development of a pricing policy and warns of threshold points where the gap between the price responsiveness of specific market subsets become considerably more pronounced.     

What do trip data reveal about bike-sharing system users?

Bike-sharing systems (BSS) have rapidly been established in many cities worldwide. The benefits that systems potentially provide are increasingly debated with concerns that the BSS may mostly benefit limited groups of citizens. Understanding how, when and by whom these systems are used may help to plan the system to be widely employed and inclusive.Trip data generated by the BSS are among the more analysed data types in the BSS literature, as they are often readily available for scholarly use. Most often, trip data are used to study the origins and destinations of the trips and their spatial patterns. In this paper, we focus on analysing how well trip data can be used to understand the demographic characteristics and usage profiles of BSS users.We first analysed the use of BSS trip data in the recent scholarly literature. We then used data from the Helsinki BSS from 2017 (~1.5 million trips) as a case to study the potential of trip data for future BSS studies. The Helsinki BSS, launched in 2016, is considered to have been a success, as it exhibits one of the highest use rates in the world. We aimed to understand how this popular system has served different user groups.We demonstrate the value of BSS trip data in understanding user characteristics and usage profiles and show that trip data have not yet been fully used for these purposes in the scholarly literature. Even considering its limitations, trip data can provide information that it is important for BSS managers and urban planners when understanding and developing the system inclusiveness. In Helsinki, we show that the BSS use is largely contributed by a limited group of people whose home area and daily travel needs likely align well with the system network. These findings point to challenges in system inclusiveness despite the internationally high use rates.     

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.     

An approach to imputing destination activities for inclusion in measures of bicycle accessibility

This paper proposes a new method to estimate bicycle accessibility for various trip purposes based on a massive dockless bike-sharing dataset in Shanghai, China. Specifically, a Dirichlet multinomial regression topic model (DMR model) is applied to identify bicycle trajectories' trip purposes, simultaneously considering arrival time and drop-off location. Based on obtained trip purposes, we estimate impedance functions using a negative exponential function. Finally, based on estimated impedance functions, two cases of bicycle accessibility for two different purposes - restaurant and hospital - are presented in Shanghais central area. The results show that almost 90% of bicycle trips are less than 30 min or 5 km. Although the difference between the impedance functions between various trip purposes is not significant, we find that trip purposes of “Work and School” have the highest travel impedance for bicyclists. Cyclists in Shanghai accept longer bicycle travel times for leisure (e.g., shopping) than for commuting (e.g., work or school).     

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations.     

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.     

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai,China

As the propensity to link multiple intermediate stops in a trip chain (a sequence of journeys that starts and ends at home, includes visiting one or more locations) is more prevalent, the relationship between travel mode choice and trip chain pattern aroused the attention of academics. This paper examines two distinct structures to identify the decision process of travelers between travel mode choice and trip chain pattern: one structure in which trip chain pattern organization precedes travel mode choice, another structure in which travel mode choice decision precedes the organization of trip chain pattern. To accommodate multi-day behavioral variability and unobserved heterogeneity in personal characteristics ignored by traditional travel surveys, multi-day GPS data collected in Shanghai is employed to estimate these two structures within Nested Logit (NL) model. The Monte Carlo (MC) method simulates the switch of trip-chaining and mode choice under possible Transportation Demand Management (TDM) strategies based on estimation results. The findings of this study are as follows: (1) trip chain pattern decision precedes travel mode choice, which means trip-chaining is organized first and affects travel mode choice; (2) complex trip chain is related to higher automobile dependency, and it is a barrier to the tendency to adopt public transit; (3) people who generally travel by automobiles might switch to public transit when private cars are unavailable, and an increase in household bicycle ownership enhances competition between the bicycle and public transit which leads people to turn to cycling. These findings help implement TDM strategies to develop sustainable transportation systems and optimize the urban trip structure.     

The decline of the traditional travel agent model

This article analyzes the struggle between traditional travel agencies and airlines to gain control of the Spanish airline services market. Also analyzed is the strong emergence of a third player, online travel portals that act as online travel agencies. We use a multinomial logit model to study the influence of 27 socio-economic factors and trip attributes on passenger purchase channel choice. The results show that the profile of a passenger who has a greater likelihood of making his/her bookings online is that of a young person, a student or educated to a high level, a habitual traveler, booking a simple journey and using an LCC. The factors linked to an increased likelihood of making purchases by phone include: being male, middle-aged, on a business or short trip, and the passengers usually use a travel agency. Finally, passengers who are over 65 years of age, with a lower academic level, who use a travel agency and are going to make a more complicated journey, are more likely to purchase their tickets in-store.     

Constraints affecting mode choices by morning car commuters

In New Zealand as elsewhere, there is an increasing interest in alleviating congestion on the road transport network to improve economic productivity, reduce pollution, and to use the transport network more effectively. Governments enact various policies to encourage car drivers to change their behaviour, but often find that the full impact is not reached. We propose that car drivers have constraints influencing their mode choice for the morning peak period trip (e.g. needing to transport children, needing a car for work during the day). A stated preference experiment conducted in the three largest New Zealand urban areas not only quantifies the likely impact of a wide range of policy tools (both ‘sticks’ discouraging car use, and ‘carrots’ encouraging alternative modes) for each area, but also identifies many significant constraints.     

Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system

In recent years, there has been increasing attention on bicycle-sharing systems (BSS) as a viable and sustainable mode of transportation for short trips. However, due to the relatively recent adoption of BSS, there is very little research exploring how people consider these systems within existing transportation options. Given recent BSS growth around the world, there is substantial interest in identifying contributing factors that encourage individuals to use these systems. The current study contributes to this growing literature by examining BSS behavior at the trip level to analyze bicyclists’ destination preferences. Specifically, we study the decision process involved in identifying destination locations after picking up a bicycle at a BSS station, using a random utility maximization approach in the form of a multinomial logit model (MNL). The quantitative frameworks developed have been estimated using 2013 data from the Chicago’s Divvy system. In our modeling effort, we distinguish between BSS users with annual membership and short-term customers with daily passes. The developed model should allow bicycle-sharing system operators to plan services more effectively by examining the impact of travel distance, land use, built environment, and access to public transportation infrastructure on users’ destination preferences. Using the estimated model, we generated utility profiles as a function of distance and various other attributes, allowing us to represent visually the trade-offs that individuals make in the decision process. To illustrate further the applicability of the proposed framework for planning purposes, destination station-choice probability prediction is undertaken.     

Short-term forecasts on individual accessibility in bus system based on neural network model

Precise forecasts on individual accessibility in bus system can help make policies to accommodate fluctuating bus travel demand and promoting social equity. In this study, we propose a three-stage method for short-term forecasts on individual accessibility in bus system based on neural network (NN) model. In the first stage, a NN model is designed to tackle the nonlinear mapping between passengers' bus trip appearances in historical periods and those in the predicted period. A rate function, which considers bus trip generation rates of passengers, is then applied using outputs of the designed NN model. In the second stage, probabilities of origin-destinations (ODs) chosen by passengers in the predicted period are calculated. In the third stage, land use information combined with results of previous two stages are used to obtain the individual accessibility in bus system in the predicted period. Compared to individual accessibility calculated by real data, it is found that the average errors of predicted results by the proposed method in weekdays and at weekends are only 8.37% and 10.13%, respectively. The results also demonstrate the capability of combining a NN model, traffic data and land use information to forecast the future spatial distribution of individual accessibility in transport system.     

Airline type and tourist expenditure: Are full service and low cost carriers converging or diverging?

Since some years ago low-cost carriers (LCCs) are becoming less and less low-cost-like, as well as full-service airlines are becoming less and less full-service-like, thus contributing to lessen the differences between users of one airline type and the other. LCCs have made air travel available to all budgets and enabled tourists to spend more at destination by reallocating their trip expenditure. The objective of this article is to observe if airline types have been converging regarding travellers’ expenditure allocation and total trip expenditure. We use repeated cross sections of the Spanish tourist expenditure survey between 2006 and 2014, and compositional data analysis with a total in order not to confound effects involving expenditure allocation with those involving expenditure volume. Results show that users of both airline types converge in their allocation of the trip budget (between transportation and at-destination expenses, and within at-destination expenses), but diverge with regard to total trip expenditure.     

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.     

Residential location and daily travel distances: the influence of trip purpose

This paper investigates the extent to which residential location influences daily distance travelled if travel purposes are differentiated. Statistical multilevel models are applied to Swedish National Travel Survey data from 2005–2006. Travel purposes are categorized by considering time–spatial constraints and hypothesized factors of personal freedom of choice. Results indicate that the influence of residential location on daily distance travelled is highly conditional on trip purpose in a nationwide Swedish context. Although statistically significant proportions of the variation in daily distance travelled to work, on service errands, and on weekdays were dependent on residential location, daily travel distances for leisure activities and on weekends varied greatly among people living in the same neighbourhood. From a policy perspective, these results suggest that measures intended to alter the built environment to reduce the volume of travel will be most efficient as regards work trips, while trips taken during free time are unlikely to be much affected. In addition, the multilevel models applied reveal several important interactions between the variation in travel distances across residential locations and individual characteristics of which researchers should be aware, especially when examining service trips.     

A multilayer model to simulate cruising for parking in urban areas

In this paper we propose an assignment model on urban networks to simulate parking choices; this model is able to simulate the impact of cruising for parking on traffic congestion. For simulating parking choice and estimating the impact of cruising on road congestion we propose a multi-layer network supply model, where each layer simulates a trip phase (on-car trip between the origin and destination zone, cruising for parking at destination zone and walking egress trip). In this model the cruising time is explicitly simulated on the network. The proposed model is tested on a trial network and on a real-scale network; numerical tests highlighted that the proposed model is able to simulate user parking choice behaviour and the impact of cruising for parking upon road congestion, particularly when the average parking saturation degrees exceed 0.7.     

Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

Traffic-related carbon dioxide (CO₂) emissions have become a major problem in cities. Especially, the CO₂ emissions induced by taxis account for a high proportion in total CO₂ emissions. The availability of taxi trajectory data presents new opportunities for addressing CO₂ emissions induced by taxis. Few previous studies have analyzed the impact of human trips on CO₂ emissions. This paper investigates trip-related CO₂ emission patterns based on individuals' travel behavior using taxi trajectory data. First, we propose a trip purpose inference method that takes into account the spatiotemporal attractiveness of POIs to divide human trips into different types. Further, we reveal the spatiotemporal patterns of CO₂ emissions from various types of trips, including temporal regularity and periodicity as well as spatial distribution of “black areas”. Finally, comparative analysis of CO₂ emissions for different kinds of trips based on trip behavior is conducted using three variables, namely trip distance, trip duration and trip speed. This study is helpful for us to understand how to make travel and cities more sustainable through modifying people's trip behaviors or taxi trips.     

Optimizing the benefits of urban road user charging

Traffic congestion is a feature of most modern cities but attempts to control it or limit its effects have met with only modest success. There is significant and continuing interest in the concept of charging city vehicle users, although apart from the use of parking charges actual operational schemes are few and far between. In this paper, we compare three alternative charging policies using a simplified model of travel demand and supply, which we combine with cost benefit techniques. The charging policies are area-based charging in which users pay to locate in or enter an area, terminal-charging based on supplementary parking fees in residential and non-residential locations and distance-based charging which is a charge related to how far users travel. The model allows for behavioural effects resulting from trip diversion and demand suppression, as well as capacity restraint (speed-flow feedback effects based on limited route capacity). In the case study, we parameterize the model using data and geographical dimensions based on London. We show that area based charging delivers the least benefits whilst a hybrid policy based on terminal and distance-based charges delivers the most. Because it is of topical interest, we compare our results and predictions with the Mayor's strategy for London, which is an area-based scheme. We conclude that the revenue generated using a hybrid policy would be as great as for an area based scheme whilst at the same time delivering substantially greater benefits to road users in terms of travel time and other savings.     

A spatial analysis of demand patterns on a bicycle sharing scheme: Evidence from London

This paper investigates the spatial demand for bikesharing through the application of a series of trip generation models for the London Bicycle Sharing Scheme (LBSS). The production of trips from and the arrival of trips at scheme stations are evaluated in reference to how they connect with features of the built environment, demographics of the resident and workplace populations, and attributes of the scheme's structure. A spatial econometrics approach is taken to specify the models, with four different time windows considered throughout the day for all trips taken during 2016. The built environment features show a consistent pattern of results in the model, indicating that proximity to cycling infrastructure, rail stations, parks, university facilities, as well as the density of shops and conventional roads in the vicinity of stations is linked with trip generation rates. The presence of males and Caucasians are associated with higher station demand, aligning with other work on the introduction of new mobility solutions elsewhere, though we do find that greater distances to work tend to depress use. Trip generation is also reduced at the minority of stations located south of the River Thames, indicating that the presence of natural barriers can affect the operation of schemes. The results carry implications for scheme integration in other cities.     

Railway trip speeds and areal coverage. The emperor’s new clothes of effectivity?

Europe’s railways have experienced a general decline in the second half of the 20th century and a renaissance since the 1980s. Total network length has decreased fundamentally while high-speed rail (HSR) line construction has soared since then. Accordingly HSR has stipulated a lot of research, but only recently HSR’s impacts on settlement structure and transport regimes have been studied in depth. We embrace a passenger’s point of view and utilize a generalized overview model for illustrating the trade-offs between trip speeds of complete trip chains, access/egress legs and mainline trip lengths. In order to estimate input parameters, an overview of railway network and settlement structure of Austria is empirically derived. In four scenarios, we vary these input parameters to study the impact of timetable integration, speed upgrades on mainlines and access/egress modes on trip speed in the Austrian case. Our results show that travel speed advantages of mainline speed increases are quickly countervailed by access/egress improvements or timetable integration improvements. We conclude that finding harmonized optimum speeds, introducing clock-face timetable design and improving access/egress modes are promising strategies in comparison to unscrutinized further maximization of mainline travel speed levels at the expense of feeder/distributor lines quality.