Identifying sources of systematic variation in direct price elasticities from revealed preference studies of inter-city freight demand

Freight demand elasticity studies vary significantly in terms of the demand measure, data type, estimation method, commodity type, etc. This wide variation makes it difficult to compare empirical estimates when the differences may arise in part from the methods and data used. In this paper we conduct a comparative analysis to identify systematic sources of influence on direct price elasticity estimates in the context of freight transport, distinguishing between road, rail, and sea transport, using published direct price elasticities from 12 elasticity-derivative studies from five countries. The study focuses on revealed preference elasticities defined by the freight rate for tonnes and tonne kilometres of inter-city freight movements. Systematic sources that explain differences in direct price elasticities include the demand elasticity measure, mode, commodity class, model estimation form, country, and temporal nature of data (e.g., cross-section). Analysts can utilise the model outputs to adjust the empirical evidence from specific studies to control for differences that impact on the behavioural implications of comparative evidence.     

A dynamic panel analysis of urban metro demand

A dynamic panel model is used to estimate the effect that fares, income and quality of service have on demand for a sample of 22 urban metros. The estimated price elasticity is −0.05 in the short run and −0.33 in the long run. The estimated long run income elasticity is small but positive (0.18), indicating that metros are perceived as normal goods. The quality of service elasticities are positive and substantially higher than the absolute value of fare elasticities. The implication is that quality of service improvements, rather than fare reductions, may be more effective in increasing metro patronage.     

Demand for cargo airships: An analysis of mode choice decision making in the freight transport industry

Despite their potential, cargo airships currently remain a theoretical concept. This research aims to examine the factors determining the demand for their services from the perspective of transport logistics professionals. An online freight mode choice experiment is conducted, supplemented with follow-up semi-structured interviews. Across freight types the sensitivity of the modes (road, rail, sea and airship) to the attributes (price, time, reliability and frequency) varied substantially. Willingness to pay for the cargo airship mode for travel time saved is estimated to be $23/tonne/hour. It is conservatively estimated that the potential market share of cargo airships in the Australian domestic freight market varies across airship models with LMH-1 having the lowest market share of 8% while ARH50 has the largest potential market share at 25%.     

Accounting for differences in modelled estimates of RP,SP and RP/SP direct petrol price elasticities for car mode choice: A warning

Using a sample of 245 direct petrol price elasticities of car travel collected from 52 published mode choice studies, a random coefficient regression model is estimated to account for heterogeneity in the influence of the type of data used in the various studies (RP, SP and a combination of RP/SP). The focus on the type of data is designed to highlight a concern that has emerged in the way that an increasing number of researchers and consultants derive elasticities from stand-alone stated preference studies, and apply them. It is well known that this is not valid without model calibration (usually via the mode-specific constants), since the elasticity formula uses the probability of an alternative being chosen. To understand the extent of possible behavioural response bias when calibration is ignored is important, signalling a possible adjustment process to correct for the absence of calibration relative to calibrated RP and or RP/SP derived elasticities.     

Temporal variance of revealed preference on-street parking price elasticity

This paper utilises revealed-preference parking trend data from parking meters ex ante and ex post of a general 50% price increase in the hourly cost of on-street parking to estimate the on-street parking price elasticity of demand in an area of Dublin, Ireland. Estimates are presented for the aggregate price elasticity of demand level and individual estimates for specific time periods and days of the week. In terms of reduced parking frequency, the average price elasticity of demand reported is −0.29. Daily average estimates are consistent, with one notable exception being Thursday, a ‘late night shopping’ day for which a lower price sensitivity is reported. Morning periods are also shown to be more responsive than other time periods in the test area, indicating some potential for influencing morning inbound peak traffic levels.     

Elasticities of demand for leisure air travel: A system modelling approach

In this study, the UK outbound demand for leisure air travel has been examined using a demand system which takes into account the ways in which the expenditure on air fares interacts with both the expenditure on non-fare components of travel abroad and with expenditure on domestic leisure. The findings suggest that there is a strong link between total consumer expenditure and expenditure on air travel with the expenditure elasticity greater than one but that, considered in aggregate, the demand for air travel is moderately inelastic with respect to air fares alone. However, the findings also show that interactions between air travel expenditure, other costs of travel abroad, as well as expenditure on leisure activities in the UK, involve sizeable cross-price elasticities.     

Experience conditioning in commuter modal choice modelling – Does it make a difference?

This paper investigates the impact of decision maker’s experience on model elasticities and predicted market share, using data collected in Sydney on commuter mode choice. Usage frequency is used as a proxy for experience and two separate mode choice models are estimated – one with experience conditioning choice and one without. Key model outputs are compared and we find that differences in the value of travel time savings and model elasticities are very marked. This suggests that ignoring experience that one has with each alternative in their choice set may be a candidate source of error in travel demand forecasts. We develop a method to obtain the level of experience for use in application of choice models to increase their prediction power.     

Low-cost airlines and airport competition

An important question from the viewpoint of competition analysis in the air transport industry is the extent to which low-cost airlines operating from a secondary airport compete with full-service airlines serving a main airport in a multiple airport region. In this paper we address the issue of the competition between full-service and low-cost airlines serving adjacent airports in the Greater London using econometric estimation of demand structure (own- and cross-price elasticities). Our analysis follows the methodology in (Pels, E., Nijkamp, P., Rietveld, P., 2000. Airport and airline competition for passengers departing from a large metropolitan area. Journal of Urban Economics, 48 (1), 29–45, Pels, E., Nijkamp, P., Rietveld, P., 2003. Access to and competition between airports: a case study for the San Francisco Bay area. Transportation Research Part A: Policy and Practice, 37 (1), 71–83). It is based on the nested logit model which we use to capture three key dimensions of passenger choice: air fare, surface-access costs and frequency. We obtained estimates of the own- and cross-price elasticities, which was the focus of our interest. On the basis of our understanding of the industry dynamics we find these estimates, especially of the cross-price elasticities, to be on the low side.     

Market segmentation analysis for airport access mode choice modeling with mixed logit

Accessing airports can be considered as a crucial issue since passengers need not miss their flights. This issue makes the mode choice to access the airports important to study on and develop policies regarding it. Many studies show destination type as domestic or international affects the airport access mode choice, along with other factors. In this study, we investigate the effect of destination type of mode choice using mixed logit, using market segmentation approach. Market segmentation regarding destination type as domestic or international is a first in airport access mode choice modeling. Revealed-preference data was collected by face-to-face passenger surveys at Ataturk International Airport in Istanbul, Turkey, in 2015. We did market segmentation analysis for Multinomial Logit (MNL) and Mixed Logit (ML) models. When MNL and ML models were compared, it was observed that ML was superior to MNL. Further, results of market segmentation analysis revealed that using segmented models produced more accurate results than using the pooled model; both in MNL and ML. This finding was also supported by the value of time estimates; there were significant differences between domestic and international travel markets in terms of airport access mode choice. These results showed that different transportation policies may be introduced for domestic and international traveler segments, which also were explained.     

A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route

This study is to analyze passengers' choice of the mode of transportation when air transportation is in competition with high speed rail (HSR). The Seoul-Jeju route analyzed as an empirical case study, in which the construction of an undersea tunnel to connect Seoul and Jeju city by HSR has been considered. The study also included two new variables, ‘safety of transportation’ and ‘availability of duty free shopping’ in addition to traditional transport choice variables such as travel time, travel costs and frequency of service to reflect special characteristics of the market. As data gathering tools, SP techniques and mixed logit model, for analytical methodology, were utilized. The authors found that the goodness of fit of the models was improved with new variables. The models also showed that the characteristics of business passengers and leisure passengers in choosing the mode of transportation were different. Business passengers were apt to choose a safety secured mode of transportation regardless of fare while leisure passengers preferred to use duty free shops more than business passengers.     

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.     

Analysis of mobility impacts of the high speed Rome-Naples rail link using withinday dynamic mode service choice models

The decrease in travel times, the better quality of the supplied services and the improved accessibility are the main factors of success of High Speed Rail links.In this paper, evidence of the impacts of the High Speed Rail line between Rome and Naples in Italy will be provided on the basis of a survey which was carried out in March 2008.A Revealed Preference (RP) survey was undertaken by Trenitalia (the Commercial Division of Italian railways) and three different questionnaires were prepared to be submitted to car users, Intercity train users and High Speed train users. They all have in common questions concerning the socioeconomic characteristics of the users, questions related to the existence of possible time constraints for the trip undertaken and questions concerning previous travel choices.Data from Trenitalia highlighted that the use of car and of Intercity trains had almost remained unchanged during the few years of operation of the High Speed service. However, a generated demand was derived from the use of this High Speed Rail link and this means that the introduction of the High Speed service between Rome and Naples probably had impacts, as excepted, on mobility choices (i.e. increase in trip frequency, new trips never done before, etc.). Data gathered were used first of all to understand this phenomenon and then to estimate a mode choice model to reproduce/forecast modal share. Specifically the choice between car and rail was modelled through a schedule based approach and with a Nested Logit model with the “train” utility function including late and early penalties.     

重庆轻轨票价方案研究

从理论上分析了影响重庆轻轨票价的主要因素,提出用拉姆齐(Ramsey)定价模型和“高峰负荷定价法”确定介于边际成本和盈亏平衡之间的最优票价。同时运用计量经济学的方法对轻轨价格需求弹性系数和运营成本做了近似估计,并为重庆轻轨制定出分段计程制票价方案。     

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.     

Congestion tolls and parking fees: A comparison of the potential effect on travel behavior

This paper presents a comparison of attitudes towards congestion and parking tolls and explores their effect on travel behavior. The findings indicate that drivers are sensitive mainly to congestion tolls and are willing to change their travel habits to avoid these tolls. The major effect of a congestion toll is the shifting of the time of the journey. High levels of demand elasticity (−1.8 for congestion tolls and −1.2 for parking fees) were found. The readiness to pay parking fees is greater; thus, the effectiveness of congestion tolls in reducing demand is higher during the times the tolls apply.     

Shopping mode choice: Physical store shopping versus e-shopping

This study aims to explore how consumers evaluate these time attributes; i.e., the value of time, when they are facing a shopping mode choice between physical store shopping and e-shopping. For this purpose, it conducts an experiment to acquire data on respondents’ stated preference choices between physical bookstore shopping and online bookstore shopping. It is finally found that the value of delivery time for a purchased book from an online bookstore to a consumer is approximately $0.53 per day, which means an online bookstore will have to lower a book’s price by $0.53 to attract a physical bookstore shopper if the delivery is delayed for one day. It is also found that in terms of monetary values, avoiding a shopping trip produces far more benefits than bearing waiting for the delivery of books for an online purchase.     

An empirical test of modal choice and allocative efficiency: Evidence from US coal transportation

A generalized shipper transportation cost function is estimated to test whether coal shippers achieve allocative efficiency with respect to market prices when facing limited access to the full range of transportation services. Findings indicate that allocative efficiency with respect to market prices is achieved when shippers have access to all major transportation modes. In contrast, the condition for allocative efficiency is not met with respect to market prices when shippers’ modal choices are limited to trucking and rail services. Findings for the sample of shippers who face limited shipping choices is interpreted as suggesting an over-use of trucks relative to the use of trains due to price distortions of transportation services.     

Constructing a mixed-logit model with market positioning to analyze the effects of new mode introduction

This study aims to investigate how the introduction of high-speed rail (HSR) influences traveler choice behavior and the market structure in the western corridor of Taiwan. A new mixed-logit model with factor decomposition is constructed to analyze the market positioning of the existing modes and HSR; the data is collected using the stated preference method. The empirical results show that travel cost is the major factor in market positioning. After the introduction of HSR, travelers’ mode preference did not change, whereas locations across modes on the market-positioning map as well as some coefficients of the explanatory variables changed.     

Mitigating the impact of errors in travel time reporting on mode choice modelling

Travel time is a major component in understanding travel demand. However, the quantification of demand and forecasting hinges on understanding how travel time is perceived and reported. Travel time reporting is typically subject to errors and this paper focuses on the mitigation of their impact on choice models. The aim is to explain the origin of these errors by including elements of travel behaviour (e.g., activities during the trip), which have been shown to significantly affect mode choices and commuting satisfaction. Based on responses from a revealed preferences survey, we estimate a mode choice model that treats travel time as a latent variable and incorporates different sources of data along with information on travel activities. Employing these multiple – sometimes incongruent – sources of information in the choice model appears to be beneficial. Results from comparing a logit model assuming error-free inputs and the integrated hybrid model revealed significant impacts on the generated policy scenarios. The model results also contributed to identifying the main travel activity features that affect travel time reporting, providing indications that can assist in understanding and mitigating the impact of imprecise measurements.     

How different is carrier choice for third party logistics companies?

The purpose of this paper is to test whether third party logistics companies (3PLs) are different from other end-shippers with respect to how they choose their carriers. The results of carrier choice models developed in this paper suggest that 3PLs are more biased against intermodal shipping than other end-shippers. The principal conclusions are as follows: mode and carrier choice modeling needs to take into consideration differences between 3PLs and other end-shippers; and with the increasing role of 3PLs in choosing carriers, their stronger bias against intermodal shipping will present further challenges to increasing freight rail mode share.