Choice behavior of tourism destination and travel mode: A case study of local residents in Hangzhou,China

The social and economic growth as result of promoting the rapid development of tourism in China has brought tremendous pressure on the urban transportation systems. Research of travel behavior concerning the characteristics of tourists has provided effective information for transportation planning. Due to different city plans, public transportation system design, car parking design and management, etc., the local situation in developed countries differs from the counterpart in China. However, little research has studied the factors influencing the choice of travel destinations in tourism. The research aims to study the tourism destination and mode choice behavior of tourists and provides suggestions to improve tourism transportation service system. An online questionnaire survey is used to collect data including the travel characteristics and personal attributes of local tourists in different holidays in Hangzhou, China. A multinomial logit model is constructed with the trip destination set as the dependent variable. Results show that age, residential type, car ownership, companion type and holiday length have a significant impact on destination choice. To determine what influences modal choice for such trips, a second logit model is established with travel mode set as the dependent variable with the explanatory variables of age, gender, companion type, car ownership, holiday length and travel destination found to be significant. The results demonstrated that people aged 26 to 44 prefer suburban areas, and they are the main group driving to their travel destination. Public transport use frequency decreases when the destination is located outside of the main tourist area. Finally, suggestions have been proposed to mitigate the congestion and parking problem based on model analysis from the perspective of the bus line setting, transfer improvements, and the policy to limit cars, respectively.     

Effects of parking and cordon charge policies on tourist's modal shift in central business district of Mashhad city,Iran

The second largest holy city of the world, Mashhad, attracts high volumes of tourists and pilgrims every year. Most visitors travel by private car and are a source of considerable funds for the local economy. Among road users, tourists as one of the major traveller categories in Mashhad city behave differently due to the particular trip purpose. The aim of this research is to model tourist's shifting modes of travel behaviour when policy measures, such as the parking and cordon fares, are implemented. The tourists’ preferences were examined using binary logit analysis when different options of travel cost and time scenarios were provided. Results indicate that travel time, parking cost, cordon cost, education level and vehicle price influence tourist's modal choice. In addition, the finding shows that congestion pricing will be more effective than a parking pricing strategy in encouraging switching of modes.     

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.     

Network-based spatial interpolation of commuting trajectories: application of a university commuting management project in Kyoto,Japan

This study presents an application of network-based spatial interpolation of student commuting trajectories from a series of origin–destination trip datasets. In particular, we incorporated multimodal public transportation networks, including bus networks, to estimate the student commuting routes. The student samples for this study were collected from an online travel diary survey conducted by Ritsumeikan University in Kyoto, Japan. The ArcGIS Network Analyst was used to construct spatial network datasets and reconstruct trajectories from the origin–destination trip dataset. In addition, line densities of estimated trajectories were calculated and displayed on maps for geovisualization. These maps helped us understand the precise locations of congestion and spatial patterns of student commuting, unlike linear representations of people’s movements that connect origins and destinations. Our study also showed that estimated trajectories can simulate quantitative impacts on travel time by promoting walking or the use of public transportation.     

A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours

In this paper we propose an improved mathematical model for locating EV charging stations. We consider the successive activities of the travelers, i.e., a person with two main stops during the day should charge the vehicle in just one of the parking spots, hence avoiding double counting the demand. The model is tested for the city of Coimbra (Portugal), where there is a network of nine stations. We conclude that our solution is better than the one that was implemented in reality, moreover we are able to conclude that demand transference has a rather significant impact on the solutions.     

Domestic tourism destination choices — a choice modelling analysis

Tourism destinations compete with each other to attract visitors. Although international tourism has received a lot of attention, domestic tourism remains the mainstay for many destinations. To inform the basis on which destinations compete, an understanding of the determinants of destination choices is required. In this paper, the discrete choice modelling method is applied to investigate the determining factors underlying the short‐break holiday destination choices of prospective tourists from Melbourne, Australia. The results from an estimated nested logit model indicate the relative importance of a number of destination and trip attributes and respondent characteristics. The model results are used to simulate the effects on destinations' market shares resulting from various changes in attributes and tourist characteristics. Copyright © 2003 John Wiley & Sons, Ltd.     

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.     

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.     

Cruising for parking

Suppose curb parking is free but all the spaces are occupied, and off-street parking is expensive but immediately available. In this case, you can cruise to find a curb space being vacated by a departing motorist, or pay for off-street parking right away. This paper presents a model of how drivers choose whether to cruise or to pay, and it predicts several results: you are more likely to cruise if curb parking is cheap, off-street parking is expensive, fuel is cheap, you want to park for a long time, you are alone in the car, and you place a low value on saving time. The model also predicts that charging the market price for curb parking—at least equal to the price of adjacent off-street parking—will eliminate cruising. Because the government sets curb parking prices, planners and elected officials strongly influence drivers’ decisions to cruise. The failure to charge market rates for curb parking congests traffic, pollutes the air, wastes fuel, and causes accidents. Between 1927 and 2001, studies of cruising in congested downtowns have found that it took between 3.5 and 14 min to find a curb space, and that between 8 and 74 percent of the traffic was cruising for parking.     

Optimisation models for the urban parking pricing problem

Parking pricing strategies are important tools for rebalancing the modal split between private car and transit systems in urban areas. Indeed, high congestion levels are mainly due to users' preference for the private car system. In order to obtain a more balanced modal split it is possible, together with an improvement in transit system quality, to set fees on the use of private cars through road pricing and/or parking pricing strategies. Parking pricing strategies are simpler, since they can be managed without adopting advanced technologies. In this paper some parking pricing strategies are proposed and some optimisation models are formalised.     

Insights into the fairness of cordon pricing based on origin–destination data

Origin–destination data are used to assess the vertical equity effects of a hypothetical road pricing zone in Canada's largest city. The assessment is based on the proportion of morning commuters affected by cordon pricing by virtue of residential location, trip destination, and travel mode. The overall findings for Toronto, Canada show that people with full-time employment and also those from higher income neighborhoods would be most affected by downtown road pricing; and this holds true when the population is broken out by gender, age group, household size and occupational class. The analysis also highlights that professionals, those who live in one- and two-person households, and those who are aged 65 and older would be disproportionately affected; those who work in manufacturing would be less affected. The equity effects of road pricing arise out of the commuting patterns of different sub-populations.     

Systematic comparison of trip distribution laws and models

Trip distribution laws are basic for the travel demand characterization needed in transport and urban planning. Several approaches have been considered in the last years. One of them is the so-called gravity law, in which the number of trips is assumed to be related to the population at origin and destination and to decrease with the distance. The mathematical expression of this law resembles Newton's law of gravity, which explains its name. Another popular approach is inspired by the theory of intervening opportunities which argues that the distance has no effect on the destination choice, playing only the role of a surrogate for the number of intervening opportunities between them. In this paper, we perform a thorough comparison between these two approaches in their ability at estimating commuting flows by testing them against empirical trip data at different scales and coming from different countries. Different versions of the gravity and the intervening opportunities laws, including the recently proposed radiation law, are used to estimate the probability that an individual has to commute from one unit to another, called trip distribution law. Based on these probability distribution laws, the commuting networks are simulated with different trip distribution models. We show that the gravity law performs better than the intervening opportunities laws to estimate the commuting flows, to preserve the structure of the network and to fit the commuting distance distribution although it fails at predicting commuting flows at large distances. Finally, we show that the different approaches can be used in the absence of detailed data for calibration since their only parameter depends only on the scale of the geographic unit.     

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.     

Exploring the role of worker income and workplace characteristics on the journey to work

Traditional trip distribution processes that rely heavily on gravity models fail to capture how the characteristics of individuals or the heterogeneity in the attributes of attraction zones may influence the accessibility to jobs and, therefore, journey-to-work patterns. Different approaches, such as destination choice models, are not generally applied because of limited data availability and calibration requirements. This paper proposes an alternative approach to overcome this challenge by combining a utility-based measure of accessibility and a maximum range of commuting distance to predict the journey-to-work patterns of individual worker-agents using an open-access database. A multinomial logit model is estimated and an agent-based model is developed using data from the Census Transportation Planning Products (CTPP) 5-year database. The proposed methodology is demonstrated using a case study based on Tippecanoe County, Indiana, and the results are compared to a double-constrained gravity model. Results indicate that the utility functions derived from the CTPP database can replicate the aggregated journey-to-work patterns by income levels. Furthermore, it was found that the utility functions for low-, middle-, and high-household income groups could be different. Finally, while a calibrated gravity model could produce a trip-length distribution and average commuting distance more similar to observed data, the destination choice model provides more insights into the trip patterns across different household income groups, thereby providing a better basis for policy analysis.     

网络拓扑结构对交通流量分布特性的影响分析

分析城市交通网络特性,建立双层城市交通网络模型,其中下层为道路网络,上层为出行网络.以节点平均负荷、拥堵节点、网络负荷标准差、基尼系数为主要指标,研究居民出行网络拓扑结构变化过程中道路网络流量分布的变化情况.研究结果显示,出行网络拓扑结构对交通量分布有较大影响,且存在最优出行网络结构,使城市交通状况达到最佳.     

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.     

Willingness to spend and road pricing rates

Using a theoretical model of urban transport system the paper examines the influence of distribution of willingness to spend within the urban population on road pricing rates. It shows that the rates that must be imposed in an urban area in order to maintain pollutant concentration and congestion due to traffic within acceptable levels is heavily dependent on the distribution of the urban population’s willingness to spend. This fact severely limits the reliability of any method for calculating road pricing rates based on theoretical analysis, so that an experimental approach seems necessary. The paper shows that a relation exists between the toll rate per kilometer of trip and the average traffic congestion, which is typical of each urban area and can be determined experimentally by successively imposing three different rates and measuring the corresponding congestion levels. The relation can then be used to determine the pricing scheme when the purpose of road pricing is to maintain both the congestion and the environmental effects due to urban traffic below acceptable thresholds. An example shows how the model can help policymakers in decision-making processes.     

Congestion affecting the dynamic of tourism demand: evidence from the most popular destinations in Spain

In this study, we propose a dynamic econometric model for tourism demand which takes into account the implications of the Tourism Area Life Cycle (TALC) theory on tourism demand. Unlike other dynamic models, in our specification the effect of the lagged demand on the current tourism demand is not constant, but dependent on congestion. We estimate the model using disaggregated data from the most visited Spanish municipalities for the period 2006–2015. Two panel data estimations are carried out: one with the coastal tourist resorts and the other one with the inland municipalities. The results show that tourism congestion reduces the positive previous tourist effect on current arrivals, suggesting that increasing congestion could worsen the attraction of a tourist destination. Congestion is more negatively perceived in inland destinations than coastal ones. Finally, a strong persistence in tourism demand for coastal destinations is shown.     

Study abroad motivations,destination selection and pre‐trip attitude formation

This study examines the role of motivations, prior travel experience, social ties and destination choice in pre‐trip attitude formation. The sample for this study is composed of a group of university students who recently participated in study abroad programs to the South Pacific or Europe. The results revealed that academic motivations and social ties influence students' destination selection for the study aboard program. Social motivation emerged as the most important factor that influences attitude toward the destinations prior to the trip. Further analysis found that the destination intended to visit mediates the effect of social motivation on pre‐trip attitude formation. Copyright © 2010 John Wiley & Sons, Ltd.     

The impact of airport parking fees on the tourist's airport/airline choice behavior

This paper analyzes the airport/airline choice behavior of tourists for Saxony/Germany. We employ flexible parametric choice methods (mixed logit) in order to test the effect of standard attributes on the choice probability. In addition we extend existing literature with the introduction of parking charges in the choice experiment. Our results show a significant and negative impact of parking charges on airport choice probability. Thus, we can compute high elasticities of parking charges for tourists. These results suggest, that airport managers have in form of parking policies a powerful policy instrument as they can directly affect the size of the airport catchment area.