A hazard-based approach to modelling the effects of online shopping on intershopping duration

Despite growing prevalence of online shopping, its impacts on mobility are poorly understood. This partially results from the lack of sufficiently detailed data. In this paper we address this gap using consumer panel data, a new dataset for this context. We analyse one year long longitudinal grocery shopping purchase data from London shoppers to investigate the effects of online shopping on overall shopping activity patterns and personal trips. We characterise the temporal structure of shopping demand by means of the duration between shopping episodes using hazard-based duration models. These models have been used to study inter-shopping spells for traditional shopping in the literature, however effects of online shopping were not considered. Here, we differentiate between shopping events and shopping trips. The former refers to all types of shopping activity including both online and in-store, while the latter is restricted to physical shopping trips. Separate models were estimated for each and results suggest potential substitution effects between online and in-store in the context of grocery shopping. We find that having shopped online since the last shopping trip significantly reduces the likelihood of a physical shopping trip. We do not observe the same effect for inter-event durations. Hence, shopping online does not have a significant effect on overall shopping activity frequency, yet affects shopping trip rates. This is a key finding and suggests potential substitution between online shopping and physical trips to the store. Additional insights on which factors, including basket size and demographics, affect inter-shopping durations are also drawn.     

The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary

Accompanying the widespread use of the Internet, the popularity of e-commerce is growing in developing countries such as China. Online shopping has significant effects on in-store shopping and on other personal activity travel behavior such as leisure activities and trip chaining behavior. Using data collected from a GPS-based activity travel diary in the Shangdi area of Beijing, this paper investigates the relationships between online shopping, in-store shopping and other dimensions of activity travel behavior using a structural equation modelling framework. Our results show that online buying frequency has positive effects on the frequencies of both in-store shopping and online searching, and in-store shopping frequency positively affects the frequency of online searching. Frequent online purchasers tend to shop in stores on weekends rather than weekdays. We also found a negative effect of online buying on the frequency of leisure activities, indicating that online shopping may reduce out-of-home leisure trips.     

Shopping online and/or in-store? A structural equation model of the relationships between e-shopping and in-store shopping

Searching product information and buying goods online are becoming increasingly popular activities, which would seem likely to affect shopping trips. However, little empirical evidence about the relationships between e-shopping and in-store shopping is available. The aim of this study is to describe how the frequencies of online searching, online buying, and non-daily shopping trips relate to each other, and how they are influenced by such factors as attitudes, behaviour, and land use features. Questionnaire data were collected from 826 respondents residing in four municipalities (one urban, three suburban) in the centre of the Netherlands. Structural equation modelling was used to examine the variables’ multiple and complex relationships. The results show that searching online positively affects the frequency of shopping trips, which in its turn positively influences buying online. An indirect positive effect of time-pressure on online buying was found and an indirect negative effect of online searching on shopping duration. These findings suggest that, for some people, e-shopping could be task-oriented (a time-saving strategy), and leisure-oriented for others. Urban residents shop online more often than suburban residents, because they tend to have a faster Internet connection. The more shopping opportunities one can reach within 10 min by bicycle, the less often one searches online.     

Modelling inter-urban transport flows in Italy: A comparison between neural network analysis and logit analysis

In the present paper a modal split problem is analysed by means of two competing statistical models, the traditional logit model and the new technique for information processing, viz. the feedforward neural network model. This study aims to explore the modal split between rail and road transport modes in Italy in relation to the introduction of a new technological innovation, the new High-Speed Train. The paper is sub-divided into two major parts. The first part offers some general considerations on the use of neural networks in the light of the increasing number of empirical applications in the specific area of transport economics. The second part describes the Italian case study by using the two above mentioned statistical models. The results highlights the fact that the two adopted models, although methodologically different, are both able to provide a reasonable spatial forecasting of the phenomenon studied. In particular, the neural network model turns out to have a slightly better performance, even though there are still critical problems inherent in its application.     

Regulating CO2 emissions of transportation in Europe: A CGE-analysis using market-based instruments

This paper analyzes the use of market-based emission regulation instruments to address the carbon dioxide emissions of transportation. Simulations with a static multi-region computable general equilibrium model show that including transportation into the European emission trading system is superior to a closed emission trading system for transportation or a tax-based approach. Furthermore, we show that exempting transportation from emission regulation is the most favorable approach in terms of welfare. This counterintuitive result is due to a large tax-interaction effect caused by high pre-existing fuel taxes in the transport sector.     

Trade-offs in CO2-oriented vehicle tax reforms: A case study of Greece

We estimate demand for automobiles in Greece using a model of product differentiation and use the model to evaluate carbon-based tax schemes that could shift consumer purchases towards low CO₂ emission cars. We find that careful policy design, supported by appropriate modeling, can bring about substantial environmental benefits without losing control of economic parameters such as public finances or firm profits. This contrasts to the results of recent vehicle tax reforms in European countries, which turned out to be more costly than initially expected.     

Disparities in childhood asthma hospitalizations: A spatial analysis of contextual effects

Transportation and health inequities have been subjects of study by scholars from various disciplines, but the linkages among traffic exposure, race, class, and asthma is neither sufficient nor conclusive. Previous studies suggest that the relationships are multifaceted and geographically specific. We investigate neighborhood social and transportation impacts on childhood asthma disparities in Tarrant County, Texas, using geographical information systems. Results reveal distinctive socio-demographic characteristics of the patients; clear spatial patterns of neighborhoods with and without asthma hospitalizations; and significant contextual effects of traffic density, race, and class on neighborhood childhood asthma hospitalizations. The findings generally support the notion of contextual effects.     

The interactions between e-shopping and traditional in-store shopping: an application of structural equations model

As the proliferation of e-commerce leads to ever greater numbers of on-line transactions, transportation planners are interested in the impacts of e-shopping on our strained transportation systems. Although the substitution effect of e-shopping is appealing, previous studies provided mixed results on its impact. Using 539 adult internet users in the Minneapolis-St Paul metropolitan area, this study applied a structural equations model to investigate the interactions among online purchases, in-store shopping, and product information search via internet. We found that online searching frequency has positive impacts on both online and in-store shopping frequencies and online buying positively affects in-store shopping. In particular, the marginal effects of online-buying frequency and online-searching frequency on in-store shopping frequency were estimated at 0.153 and 0.189, respectively. Since the internet as a shopping channel tends to have a complementary effect on in-store shopping, the rise of e-shopping is not likely to be a solution but a challenge to travel reduction.     

Effect of land use on decisions of shopping tour generation: A case study of three traditional neighborhoods in WA

This study investigates the relationship between land use and shopping tour generation using an activity-based shopping model that captures the effects of land use patterns on household decisions of shopping tour frequency, tour scheduling and mode choice. The model was calibrated using travel data collected in three traditional neighborhoods located in the Puget Sound region, WA, and shopping travel patterns across seven common household structures were analyzed. The results reveal that land use patterns have virtually no impact on overall shopping tour frequency. However, land use does seem to be associated with decisions about the type of shopping tours undertaken. For example, households with poorer accessibility tend to make fewer one-stop shopping tours, and are more likely to combine shoppingtrips with other trips to form multi-stop shopping tours as a means of compensating for locational deficiencies. Finally, we also found that traditional neighborhood residents who live closer to the neighborhood commercial street, and thus, have greater accessibility, are more inclined to use non-auto modes for one-stop shopping tours.     

The impacts of E-retail on the choice of shopping trips and delivery: Some preliminary findings

E-retail, like many other information technology-based activities (telecommuting, telemedicine etc.) offers a potential substitution of travel by telecommunications. Traditional shopping activities typically consist of a visit to a store in which product information is sought, and a decision on purchase is made. Pending that decision, the product is obtained and most often self-delivered by the consumer. Certain types of products are store-delivered to the consumer premises. In the face of E-retail, consumers can acquire information, make a purchase transaction and choose a delivery arrangement from a remote location. These options may result in a reduction of transport activity, as a delivery by the supplier is potentially more efficient than the traditional process. The current study presents a conceptual model of the decisions households make with regard to information gathering, purchase transactions and delivery mode. Data on revealed behavior and various socio-demographic and economic characteristics of shoppers was collected in the Tel-Aviv Metropolitan area in the summer of 2004.     

A comparison of accuracy and cost of attribute data in analysis of transit service areas using GIS

User oriented transit service is designed to meet the particular needs of a selected group of travelers. Transit Routes are located to provide convenient linkages between user's origin and destination in such a way that out-of-vehicle time, such as access and transfer time, is minimized. Planning transit routes requires understanding demographics, land use and travel patterns in an area. The dynamic nature of these systems necessitates regular review and analysis to insure that the transit system continues to meet the needs of the area it serves. Geographic Information Systems (GIS) provide a flexible framework for planning and analyzing transit routes and stops. Socioeconomic, demographic, housing, land use, and traffic data may be modeled in a GIS to identify efficient and effective corridors to locate routes. Part of the route location and analysis problem requires estimating population within the service area of a route. A route's service area is defined using walking distance or travel time. The problem of identifying service areas for park and ride or auto/bus users is not considered here, but assumed analogous to walk/bus trips. This paper investigates the accuracy and costs associated with the use of different attribute data bases to perform service area analysis for transit routes using GIS. A case study is performed for Logan, Utah, where a new fixed route service is operated. The case study illustrates the use of census data, postal data, data collected from aerial photographs, and data collected during a field survey using the network area analysis technique for transit service area analysis. This comparison allows us to describe the amount of error introduced by various spatial modeling techniques of data bases representing a variety of aggregation levels.     

Enjoyment of commute: A comparison of different transportation modes

This study investigates how socio-demographic and attitudinal variables of university students affect their desire to increase or decrease their daily commute. The case study is McMaster University in Hamilton, Canada, and data was obtained by means of a web-based survey that included questions regarding travel behavior, socio-demographic information, and attitudes toward travel, land use, and the environment. The objective variable is defined as the ratio of ideal to actual commute time, and regression analysis is implemented to test the relationship between this variable and socio-demographic variables and attitudinal scores. The impact of different attitudes on the gap between ideal and actual commute time is expanded to include three different modes, active travel (walk/cycle), transit, and personal automobile. Interestingly, the results indicate that active travelers tend to be less dissatisfied with their commute, followed by those who travel in a personal vehicle and transit users. A number of attitudinal responses are shown to impact the desire to travel more or less, including variables that relate to the social environment, availability of local activities, quality of facilities, productive use of the commute, and the intrinsic value found in the commute travel. The picture emerges of a traveler who would like to spend more time commuting, as someone who is an active traveler, thinks that getting there is half the fun, dislikes traveling alone, but rather likes to live in an active neighborhood where there is a sense of community. The results suggests that enjoyment of commuting, while a challenge from the perspective of motorized mobility, may provide valuable policy opportunities from the perspective of active transportation.     

Voluntary CO2 emissions reduction scheme: Analysis of airline voluntary plan in Japan

This paper discusses voluntary CO₂ emissions reduction schemes and, in particular focuses on the voluntary plan by the Japanese airline industry. Econometric analysis identifies statistically significant improvement of 3–4% in CO₂ emissions intensity (CO₂/RPK) subsequent to initiation of the voluntary plan in 1998.     

Conventional,hybrid and electric vehicles for Australian driving conditions. Part 2: Life cycle CO2-e emissions

This paper is the second of a two part study which quantifies the economic and greenhouse performance of conventional, hybrid and fully electric passenger vehicles operating in Australian driving conditions. This second study focuses on the life cycle greenhouse gas emissions. Two vehicle sizes are considered, Class-B and Class-E, which bracket the large majority of passenger vehicles on Australian roads.Using vehicle simulation models developed in the first study, the trade-offs between the ability of increasingly electric powertrains in curtailing the tailpipe emissions and the corresponding rise in the embedded vehicle emissions have been evaluated. The sensitivity of the life cycle emissions to fuel, electricity and the change in the energy mix are all considered. In conjunction with the total cost of ownership calculated in the companion paper, this allows the cost of mitigating life cycle greenhouse gas emissions through electrification of passenger transport to be estimated under different scenarios. For Class-B vehicles, fully electric vehicles were found to have a higher total cost of ownership and higher life cycle emissions than an equivalent vehicle with an internal combustion engine. For Class-E vehicles, hybrids are found to be the most cost effective whilst also having lowest life cycle emissions under current conditions. Further, hybrid vehicles also exhibit little sensitivity in terms of greenhouse emissions and cost with large changes in system inputs.     

Towards marginal cost pricing: A comparison of alternative pricing systems

European urban areas are marred by the problems of congestion and environmental degradation due to the prevailing levels of car use. Strong arguments have thus been put forward in support of a policy based on marginal cost pricing (European Commission 1996). Such policy measures – which would force private consumers to pay for a public service that was previously provided "for free" – are, however, notoriously unpopular with the general public and hence also with their elected representatives – the politicians. There is thus an obvious tension between economic theory, which suggests that marginal cost pricing is the welfare maximising solution to urban transport problems, and practical experience, which suggests that such pricing measures are unwanted by the affected population and hence hard to implement through democratic processes. The AFFORD Project for the European Commission has aimed to investigate this paradox and its possible solutions, through a combination of economic analysis, predictive modelling, attitudinal surveys, and an assessment of fiscal and financial measures within a number of case study cities in Europe. In this paper the methodology and results obtained for the Edinburgh case study are reported in detail. The study analyses alternative road pricing instruments and compares their performance against the theoretical first best situation. It discusses the effect of coverage, location, charging mechanism and interaction with other instruments. The paper shows that limited coverage in one mode may lead to a deviation from the user pays principle in other modes, that location is as important as charge levels and that assumptions about the use of revenues are critical in determining the effect on equity and acceptability. Finally the results show that a relatively simple smart card system can come close to providing the economic first best solution, but that this result should be viewed in the context of the model assumptions.     

Marginal cost pricing of truckload services: A comparison of two approaches

A useful input to the pricing of truckload trucking services is the marginal benefit to the entire system of an additional load going from one region to the next. Two approaches are reviewed for estimating the marginal system benefit. The first is based on a well-known deterministic network formulation in which certain easily obtainable shadow prices provide an estimate of the marginal benefit. The second is based on a nonlinear stochastic formulation which incorporates uncertainty in demand forecasts. Each procedure is reviewed in detail and evaluated in terms of the accuracy with which the actual change in total system profit (found by increasing the demand on a link and completely reoptimizing) matches the estimated benefit.     

An empirical comparison of disaggregate category and regression trip generation analysis techniques

Category and regression household trip generation analysis techniques were compared and contrasted. The comparative research was facilitated through a discussion that revealed the interchangeability of two methods of calibrating a category model. While the cell mean method is simple to implement, it does not readily yield statistical indexes for comparison with regression models. The general linear model analysis of variance (GLANOVA) readily provides statistical indexes for the comparison of category and regression trip generation models, and it produces identical empirical results to the simpler cell mean approach of calibrating a category model.The empirical comparison supports the widespread use of category models for trip generation analysis in transportation planning studies. It was found that regression and category models yielded equivalent results for typical planning applications at the district level of aggregation. In addition, both techniques estimated overall trip rate with equal accuracy in the calibration phase, and the two approaches were indistinguishable with respect to sample size sensitivity. However, households with extremely large trip rates were underestimated to a greater degree by category models than regression models. This tendency, in turn, resulted in larger calibration coefficients of determination for regression models. Since the cell mean method of calibrating a model is simpler and easier to understand than a regression model representation, category models can be recommended over regression models for planning studies.     

Congestion and emissions mitigation: A comparison of capacity,demand, and vehicle based strategies

Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation does not inevitably lead to reduced emissions; the net effect of mitigation depends on the balance of induced travel demand and increased vehicle efficiency that in turn depend on the pollutant, congestion level, and fleet composition. In the long run, capacity-based congestion improvements within certain speed intervals can reasonably be expected to increase emissions of CO₂e, CO, and NO_x through increased vehicle travel volume. Better opportunities for emissions reductions exist for HC and PM_2.5 emissions, and on more heavily congested arterials. Advanced-efficiency vehicles with emissions rates that are less sensitive to congestion than conventional vehicles generate less emissions co-benefits from congestion mitigation.     

Assessing the CO2 impact of current and future rail track in the UK

This paper investigates the CO₂ impact of current and future UK rail track and estimates the material, process and transport emissions associated with construction, maintenance and end-of-life activities for designs at high and low traffic loads. Analysis shows that for current track configurations, track with concrete sleepers has the lowest CO₂ impact, followed by steel, hardwood and softwood. Several potential future rail track designs have been analysed including embedded rail and double and quadruple-headed rail. All future track designs have a lower impact than current designs, but this improvement is more marked at high traffic loads. Up to a 40% reduction in CO₂ impact could be achieved if the UK rail network was to move from conventional track design to a double-headed embedded rail design. Key levers for reducing the CO₂ impact of track are identified as service life extension, traffic load reduction and the selection of low impact track designs.     

Structure and influencing factors of CO2 emissions from transport sector in three major metropolitan regions of China: estimation and decomposition

Transportation - The three metropolitan regions, including Jing-Jin-Ji (JJJ), Yangtze River Delta (YRD) and Pearl River Delta (PRD), are the most developed regions of China. This study focuses on...