Generalized transport costs and index numbers: A geographical analysis of economic and infrastructure fundamentals

We rely on the economic theory approach to index numbers to improve the existing definitions and decompositions of variations in generalized transport costs (GTCs). As a value index, we decompose GTCs into price and quantity indices associated to economic—market—costs and infrastructure variables—distance and time within a network. The methodology allows the accurate identification of the sources of GTCs decline. We illustrate it for the case of road freight transportation in Spain between 1980 and 2007 and at a highly detailed geographical level. Average GTCs weighted by trade flows have decreased by 16.3%, with infrastructure driving that reduction. We find large territorial disparities in GTCs, but also significant geographical clusters where the market and network indices show spatial association.     

Estimating the environmental benefits of ride-sharing: A case study of Dublin

Ride-sharing has often been cited as a successful method to reduce congestion and green house gas emissions. This paper examines the patterns of ride-sharing, in Dublin, and estimates the environmental benefits of ride-sharing both in terms of reductions in emissions and the vehicle kilometres travelled. Data from the 2006 Census of Ireland is used to examine the travel patterns of those that ride-share. The COPERT4 model is used in this paper to estimate the CO₂ emissions saved by ride-sharing.     

Congestion and pollution consequences of driving-to-school trips: A case study in Beijing

Parents compete for high-quality education for their children by enrolling them in good schools. However, in a Chinese mega-city like Beijing, three factors jointly lead to the spatial separation between schools and homes: the centralized public goods provision mechanism, the historical dependency in school location, and the constrained supply of housing in downtown. Without an adequate number of school buses, this spatial separation of schools and homes triggers the numerous long-distance driving-to-school trips by private vehicle during workday morning rush hours in Beijing. We use the start and end dates of “school holiday” as exogenous repeated shocks to the aggregate traffic congestion, and employ the two-stage least squares (2SLS) regression approach to examine the congestion and pollution consequences of such driving-to-school trips in Beijing. We find that, all else being equal, workdays during school holidays have a traffic congestion index 20% lower than that of non-school-holiday workdays. Such a sharp reduction in congestion leads to a significant decrease in PM₁₀ concentration. Policymakers should lower such “extra” congestion and environmental costs via optimizing the spatial balance between school supply and demand.     

Quantifying the environmental and economic benefits of cooperation: A case study in temperature-controlled food logistics

Inefficient road transportation causes unnecessary costs and polluting emissions. This problem is even more severe in refrigerated transportation, in which temperature control is used to guarantee the quality of the products. Organizing logistics cooperatively can help decrease both the environmental and the economic impacts. In Joint Route Planning (JRP) cooperation, suppliers and customers jointly optimize routing decisions so that cost and emissions are minimized. Vendor Managed Inventory (VMI) cooperation extends JRP cooperation by optimizing routing and inventory planning decisions simultaneously. However, in addition to their economic advantages, VMI and JRP may also yield environmental benefits. To test this assertion, we perform a case study on cooperation between a number of supermarket chains in the Netherlands. The data of this case study are analyzed to quantify both the economic and environmental benefits of implementing cooperation via JRP and VMI, using vehicle routing and an inventory routing models. We found that JRP cooperation can substantially reduce cost and emissions compared with uncooperative routing. In addition, VMI cooperation can further reduce cost and emissions, but minimizing cost and minimizing emissions no longer result in the same solution and there is a trade-off to be made.     

Development of commuter and non-commuter mode choice models for the assessment of new public transport infrastructure projects: A case study

This paper uses state of the art stated choice designs to parameterise modal choice models for commuting and non-commuting travel futures in the presence of new public transport infrastructure (variations of new heavy rail, light rail and dedicated busway systems). D-optimal choice experiments are developed for a set of labelled modal alternatives in which respondents establish a reference benchmark based on the existing service levels (for access, linehaul and egress trip legs) which is used in a computer aided personal interview instrument to generate future scenarios of service levels for current and prospective new modals options. We show that a fully integrated stated choice experiment provides all the information required to obtain behaviourally relevant parameter estimates (within a nested logit framework) for all but the mode-specific constants (MSCs). The MSCs can be calibrated for the current modes within a network model setting, giving the transport planner an appropriate model for predicting the patronage potential for proposed new public transport infrastructure services. A useful by-product is a new set of behavioural values of travel time savings for access, egress, linehaul and wait times.     

Estimating the bus user time benefits of implementing a median busway: Methodology and case study

This paper presents a general framework to estimate the bus user time benefits of a median busway including the effects on travel time and access time. Unlike previous models, we take into account the effects of geometry and the interaction with the demand structure. Models for predicting the bus in-vehicle time benefits of a median dual carriageway busway against mixed traffic condition on 2 and 3 lanes roads are estimated using data from a case study in Santiago (Chile), using a bus travel time model empirically estimated and considering different base case situations, including mixed traffic operations and bus lanes. Results of the application show that the expected in-vehicle time savings of a median busway might be reduced by access time losses due to increased walking distances and road crossing delays. Also, that net time benefits can vary significantly according to the base situation and the structure of demand considered. These findings point out to the need of including a wider set of impacts when studying the benefits of median busways, beyond in-vehicle time savings only. The empirical work presented here is completely based on passive data coming from GPS and smartcards, what makes easier and cheaper to conduct this type of analysis as well as to do it with a comprehensive scope at an early stage of the development of a BRT project. This framework can be extended to other types of dedicated bus lanes provided that a corresponding bus travel time savings model is available.     

Public financing of private freight rail infrastructure to reduce highway congestion: A case study of public policy and decision making in the United States

As goods movement continues to increase it is expected to outpace infrastructure capacity in the United States. Moving a larger share of goods by rail rather than truck is a potentially cost effective part of a solution. Freight rail not only offers a substitute for truck trips but is a cleaner, more energy efficient, and safer alternative. Recently a number of private freight rail projects have received public funding. The public funds are aimed at increasing freight rail capacity with the goal of diverting some goods currently moved by truck to rail. While the benefits of moving goods by rail are relatively clear, it is unclear if public decision makers can effectively identify strategic rail investments that will achieve their policy goals. This study critically examines the analytical methods, models, and data that are commonly used to support decisions to provide public funds for private freight rail projects. This is accomplished through a case study of California’s Trade Corridors Improvement Fund program which provided $680 million for 11 freight rail projects. The study’s contributions include identifying critical analytical flaws and challenges affecting the benefit estimates that public funding decisions rely on. Improvements to current evaluation methods are also identified as are regulatory reforms and policy interventions that may offer more effective and reliable outcomes.     

Short-term prediction of border crossing time and traffic volume for commercial trucks: A case study for the Ambassador Bridge

Short-term forecasting of traffic characteristics, such as traffic flow, speed, travel time, and queue length, has gained considerable attention from transportation researchers and practitioners over past three decades. While past studies primarily focused on traffic characteristics on freeways or urban arterials this study places particular emphasis on modeling the crossing time over one of the busiest US–Canada bridges, the Ambassador Bridge. Using a month-long volume data from Remote Traffic Microwave Sensors and a yearlong Global Positioning System data for crossing time two sets of ANN models are designed, trained, and validated to perform short-term predictions of (1) the volume of trucks crossing the Ambassador Bridge and (2) the time it takes for the trucks to cross the bridge from one side to the other. The prediction of crossing time is contingent on truck volume on the bridge and therefore separate ANN models were trained to predict the volume. A multilayer feedforward neural network with backpropagation approach was used to train the ANN models. Predicted crossing times from the ANNs have a high correlation with the observed values. Evaluation indicators further confirmed the high forecasting capability of the trained ANN models. The ANN models from this study could be used for short-term forecasting of crossing time that would support operations of ITS technologies.     

A comparative study of a hybrid Logit–Fratar and neural network models for trip distribution: case of the city of Isfahan

This paper introduces a new procedure to forecast the future O/D demand. It is a hybrid of logit and Fratar model. The hybrid model has the long run, policy sensitive, characteristic of a logit model, calibrated at sector‐level with little/no zero O/D cells. This feature, joint with a Fratar‐type operation at zonal level within a sector, gives a better performance to this model than either of the two types of the models alone. The performance of the hybrid model is contrasted with a neural network model, and shows encouraging results in a real case. Copyright © 2010 John Wiley & Sons, Ltd.     

Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing,China

In this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models – the set covering model, the maximal covering location model, and the p-median model – and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p-median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p-median and the maximal covering location models, our results suggest that (1) the p-median model outperforms the maximal covering location model in terms of satisfying the other’s objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases.     

Estimation of cost and CO2 emissions with a sustainable cross-border supply chain in the automobile industry: A case study of Thailand and neighboring countries

CO₂ emissions are increasing because of the growth in the cross-border supply chain, which is leading the locations of assembly plants and suppliers to spread across a wider area. Given that one passenger vehicle needs more than 20,000 components and parts, the automobile industry exploits the cross-border supply chain. Recently, the free cross-border movement of people, goods, capital, and information has accelerated in Asia. Therefore, a sustainable cross-border supply chain is required to reduce both CO₂ emissions and cost. This study estimates total CO₂ emissions per vehicle including production and transportation processes in Thailand and neighboring countries and the change in CO₂ emissions based on future policy scenarios that consider the automobile market and locational conditions in 2030. The results show that locating production close to the place of consumption and the electricity emissions factors in each country should be considered.     

A three-stage least squares approach to the analysis of airline strategies for aircraft size and airline frequency on the north Atlantic: an airline case study

In response to increasing demand, airlines may increase capacity by increasing the frequency of flights or they may choose to increase aircraft size. This may yield operating cost economies. If the airports they operate from are capacity constrained, they will be limited in the extent that they can change frequency which will limit their ability to compete with the number of frequencies offered. This article focuses on this trade-off and pays particular attention to the practices of a specific airline. Conclusions are offered on the impact of inter alia competition, changes in aircraft technology, 9/11 and the impact of slot constraints. It appears that changes in size are more important than frequency, which is consistent with the presence of slot constraints and there is a significant impact of competition. As the concentration of carriers increases, so aircraft size falls. 9/11 also has a significant impact on traffic whereas the introduction of the Boeing 777, as an illustration of a change in technology, does not.