Fuel demand,road transport pollution emissions and residents’ health losses in the transitional China

In recent years, China’s rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents’ health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed significant impacts on the China’s road transport sector. Therefore, inspired by Li and Zhou (2005), we propose an assumption that China’s provincial economies are independent “economic entities”. Based on this assumption, we investigate the China’s road transport fuel (i.e., gasoline and diesel) demand system by using the panel data of all 31 Chinese provinces except Hong Kong, Macau and Taiwan. To connect the fuel demand system and the air pollution emissions, we propose the concept of pollution emissions elasticities to estimate the air pollution emissions from the road transport sector, and residents’ health losses by a simplified approach consisting of air pollution concentrations and health loss assessment models under different scenarios based on real-world oil price fluctuations. Our framework, to the best of our knowledge, is the first attempt to address the transmission mechanism between the fuel demand system in road transport sector and residents’ health losses in the transitional China.     

Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: a hybrid choice modelling approach

Cycling is often promoted as a means of reducing urban congestion and improving health, social and environmental outcomes. However, the quantification of these potential benefits is not well established. This is due in part to practical difficulties in estimating cycling demand and a lack of sound methodologies to appraise cycling initiatives. In this paper we attempt to address this need by developing predictive models of cycle demand, relative to other transport modes, that capture not only the impacts of observed characteristics such as age and travel time but also the role of attitudes and perceptions. Using data from a stated preference survey, we estimate a hybrid choice model for cycle use that incorporates the role of attitudes towards cycling, perceptions of the image associated with cycling, and the stress arising from safety concerns. Model results indicate that the latent attitudes and perceptions explain an important part of the non-observable utility in a simple multinomial logit choice model. We also demonstrate policy analysis using the hybrid choice model, which allows comparisons of ‘hard’ policies such as the provision of parking facilities against ‘soft’ measures such as cycle promotion schemes.     

Bayesian estimation of multinomial probit models of work trip choice

In this paper, we estimate a multinomial probit model of work trip mode choice in Seoul, Korea, using the Bayesian approach with Gibbs sampling. This method constructs a Markov chain Gibbs sampler that can be used to draw directly from the exact posterior distribution and perform finite sample likelihood inference. We estimate direct and cross-elasticities with respect to travel cost and the value of time. Our results show that travel demands are more sensitive to travel time than travel cost. The cross-elasticity results show that the bus has a greater substitute relation to the subway than the auto (and vice versa) and that an increase in the cost of an auto will increase the demand for bus transport more so than that of the subway.     

Assessing the financial and social costs of public transport in differing operating environments and with endogenous demand

This paper uses a previously developed spreadsheet cost model which simulates public transport modes operated on a 12-km route to analyse the total costs of different passenger demand levels. The previous cost model was a very powerful tool to estimate the social and operator costs for different public transport technologies. However, as the model is strategic, some basic assumptions were made which are relaxed in this paper. First, the speed-flow equation in the original spreadsheet model assumes that speed decreases according to the ratio of the current frequency and the lane capacity which is based on the safety headway without taking into account passenger boardings. However, this may vary in different operating environments. Therefore, the speed-flow equation is improved by moving from a linear equation to a piecewise equation that considers the features of different operating environments. Second, the model assumes that supply is sufficient to meet demand. However, when the level of demand is high for the lower-capacity public transport technologies, passengers may find the incoming vehicle full and therefore, they have to wait more than one service interval. This paper applies queuing theory to investigate the probability of having to wait longer than the expected service headways which will affect the average passenger waiting time. The extra waiting time for each passenger is calculated and applied in the spreadsheet cost model. Third, the original model assumed that demand was externally fixed (exogenous). To evaluate the differences after applying these equations, endogenous demand rather than exogenous demand will be investigated by using the elasticities for passenger waiting time and journey time.     

Do travel cost models value transportation properly?

This paper examines whether travel cost models value transportation properly. It uses contingent behavior and contingent pricing analyses to explore the valuation of transport costs within the context of recreation demand. The contingent behavior analysis poses hypothetical increases in travel costs––travel distance and access fees––and examines the demand responses. In contrast, the ‘contingent pricing’ analysis asks respondents to state the increase in travel costs that is consistent with certain reductions in recreation demand, in this case, reductions that eliminate demand. By comparing distance-related responses to fee-related responses, the two analyses estimate factors for testing and improving the valuation of transport costs. To achieve these ends, the two analyses also explore the valuation of time costs.     

Institutional issues in planning for more uncertain futures

Future travel demand has always been difficult to estimate. Recent trends of a slow down or stagnation in traffic growth combined with substantial demographic, economic, and technological shifts further complicate that task. This poses a significant planning challenge given that decision-making is often based on the benefits of infrastructure investments which accrue over periods as long as 60 years. In contrast to the changing ideas around what mobility in the future will look like and the types of demand it may need to service, the practice of forecasting future travel demand remains largely unchanged as do the decision-making processes which flow from this. Alternative approaches to thinking about futures such as scenario planning exist but have had more limited deployment in the transport sector. This paper explores the institutional issues surrounding the purpose, practice and barriers to changing the approaches of forecasting and decision-making through an exploration of the state of practice in the UK drawing on interviews with 23 practitioners. Drawing on Hall’s work on policy change, the research finds that there is a strong policy paradigm built around the relationships between transport investment and traffic growth. This has strengthened since the recession with an increased focus on the relationship between transport and job creation. The forecasting approaches in use today are an important part of a complex decision-making apparatus reflective of specialized policy arenas like transport. Challenges in acknowledging, representing and communicating uncertainty are identified leaving a growing tension between planning visions and planning practice. We conclude by reflecting on events which may stimulate a broader reframing of how we plan for transport futures whilst embracing key uncertainties.

     

Crowding in public transport systems: Effects on users,operation and implications for the estimation of demand

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.     

Empirical estimation of price and income elasticities of air cargo demand: The case of Hong Kong

This paper estimates the price and income elasticities of air cargo demand and examines how they may change after the 2008 financial crisis. Using a set of time series data, we simultaneously estimate the aggregated demand and supply functions of air cargo at Hong Kong International Airport (HKIA). We find that during the entire sampling period of 2001–2013, the price elasticity for air cargo transport demand at HKIA ranges from −0.74 to −0.29, suggesting that air cargo demand in Hong Kong reacts negatively to price (as expected) but does not appear to be very sensitive to price. The income elasticity ranges from 0.29 to 1.47 and appears sensitive to seasonality adjustment approaches. However, in terms of the speed of changes, air cargo demand changes much faster than overall economy, indicating the presence of a pro-cyclical pattern of air cargo traffic with respect to the overall economy. Our analysis shows that air cargo demand becomes more sensitive to changes in both price and income after 2008.     

Effect of open skies in the Middle East region

This research aims to estimate potential inter-regional passenger flows for air transport in the Middle East under open skies polices, once deregulation agreements are reached between neighboring countries. To arrive at reasonable demand estimates, Western and Eastern European demand data was analyzed as a first step, since it is assumed that current Middle Eastern demand is distorted as a direct result of regional political instability. The major factors affecting demand, based on the European dataset, included population size, gross domestic product (GDP) per capita, absolute difference in GDP per capita between two countries, great circle distance and membership of the European Union and World Trade Organization. Subsequently, a 21 country database was estimated for passenger flow in the Middle East region on an average peak season day. The demand estimations became input for a hub location model (p-hub median formulation) in order to achieve the second major aim of this research, objective identification of potential regional gateways. The results proved robust to both single and multiple allocation model assumptions, with Cairo and Tehran consistently achieving hub status, along with Istanbul and Riyadh, as the number of potential hubs increased. Finally, this research shows that under conditions of peace, given existing socio-economic indicators, inter-regional passenger demand flow could increase by upwards of 51% and regulatory authorities ought to consider the necessary infrastructure and demand management policies to enable the conservative regional demand growth estimated.     

The suitability of hedonic models for cost-benefit analysis: Evidence from commuting flows

We compare two estimates of benefits arising from the construction of new bridges in south-west Norway. One estimate comes from a hedonic property value model. Rather than follow an approach which is strictly theoretically correct, we adopt Rosen’s simple first-stage approach. To investigate and validate whether this simplified approach gives a reasonable estimate, we compare it to an estimate derived from a travel demand model. We find that a variant of an ex post hedonic house price model gives very similar estimates to the estimates from the travel demand model. This supports a hypothesis that the simplistic hedonic approach is reasonable.     

A model of the vicious cycle of a bus line

It has been frequently noted that in a non-regulated environment the development of public transport service is self-adjusting: Faced with decreasing demand, operators will tend to reduce service to cut costs, resulting in a decrease in the level-of-service, which then triggers a further drop in demand. The opposite may also occur: high demand will induce the operator to increase supply, e.g. through an increase in frequency, which results in a higher level-of-service and a subsequent increase in passenger numbers, triggering another round of service improvements. This paper adds to the literature by presenting an analytic model for analyzing these phenomena that we call vicious and virtuous cycles. Based on field data regarding passengers’ variation in willingness-to-wait for a public transport service, we investigate the dynamics of the line service and show how the emergence of a vicious or virtuous cycle depends on the total number of potential passengers, the share of captive riders, and bus capacity. The paper ends with a discussion of the implications of the findings for the planning of public transport services.     

Incorporating Logistics in Freight Transport Demand Models: State-of-the-Art and Research Opportunities

Freight transport demand is a demand derived from all the activities needed to move goods between locations of production to locations of consumption, including trade, logistics and transportation. A good representation of logistics in freight transport demand models allows us to predict the effects of changes in logistics systems on future transport flows. As such it provides better estimations of the costs of interaction and allows to predict changes in spatial patterns of freight transport flows more accurately. In recent years, the attention for freight modelling has been growing and new research work has appeared aimed at incorporating logistics in freight models. In this paper we review the state of the art in the representation of logistics considerations in freight transport demand models. Our focus is on the service and cost drivers of changes in logistics networks and how these affect freight transport. Our review proceeds along a conceptual framework for modelling that goes beyond the conventional 4-step modelling approach. We identify promising areas for freight modelling that have an integrative function within this framework, such as spatial computable general equilibrium models, supply chain choice models and hypernetwork models.     

Modelling mode choice for freight transport using advanced choice experiments

In this paper we use advanced choice modelling techniques to analyse demand for freight transport in a context of modal choice. To this end, a stated preference (SP) survey was conducted in order to estimate freight shipper preferences for the main attributes that define the service offered by the different transport modes. From a methodological point of view, we focus on two critical issues in the construction of efficient choice experiments. Firstly, in obtaining good quality prior information about the parameters; and secondly, in the improved quality of the experimental data by tailoring a specific efficient design for every respondent in the sample.With these data, different mixed logit models incorporating panel correlation effects and accounting for systematic and random taste heterogeneity are estimated. For the best model specification we obtain the willingness to pay for improving the level of service and the elasticity of the choice probabilities for the different attributes. Our model provide interesting results that can be used to analyse the potential diversion of traffic from road (the current option) to alternative modes, rail or maritime, as well as to help in the obtaining of the modal distribution of commercial traffic between Spain and the European Union, currently passing through the Pyrenees.     

Valuing crowding in public transport: Implications for cost-benefit analysis

This paper investigates the valuation of crowding in public transport trips and its implications in demand estimation and cost-benefit analysis. We use a choice-based stated preference survey where crowding levels are represented by means of specially designed pictures, and use these data to estimate flexible discrete choice models. We assume that the disutility associated with travelling under crowded conditions is proportional to travel time. Our results are consistent with and extend previous findings in the literature: passenger density has a significant effect on the utility of travelling by public transport; in fact, the marginal disutility of travel time in a crowded vehicle (6 standing-passengers/m²) is 2.5 times higher than in a vehicle with available seats. We also compare the effects of different policies for improving bus operations, and the effect of adding crowding valuation in cost-benefit analysis. In doing that, we endogenise the crowding level as the result of the equilibrium between demand and supplied bus capacity. Our results indicate that important benefits may be accrued from policies designed to reduce crowding, and that ignoring crowding effects significantly overestimate the bus travel demand the benefits associated with pure travel time reductions.     

Behavioral monitoring of public transport users through a mobile communication system

There are various activities now taking place in ITS research and development in Japan. Advanced information and communication technologies have been applied to improve public transport systems, as well as automated highway systems. In the first part of this paper, we show three examples of public transport systems recently developed in ITS environment. These transport systems are operated in local cities and towns in Japan: the travel information system for tram users in Hiroshima, the demand responsive bus system in Nakamura and the co‐operative use of electric vehicle in Ebina. In the second part of the paper, we explain how we have monitored individual passenger on public transport using cellular phones for location positioning. Location positioning technology for mobile object is essential for the operation and management of ITS supported public transport systems. Furthermore, such accurate and detailed positioning data can be utilized for travel behaviour analysis in demand modeling. The mobile instrument and monitoring systems shown in this paper can be combined with any of the case studies of ITS application to public transport systems.     

Induced road traffic in Spanish regions: A dynamic panel data model

Distinguishing between traffic generated exclusively from the expansion of the road network (induced demand) and that resulting from other demand factors is of crucial importance to properly designed transport policies. This paper analyzes and quantifies the induced demand for road transport for Spain’s main regions from 1998 to 2006, years that saw mobility in Spain attain its highest growth rate. The lack of research in this area involving Spain and the key role played by the sector, given its high level of energy consumption and the negative externalities associated with it (accidents, noise, traffic congestion, emissions, etc.), endow greater relevance to this type of research. Based on a Dynamic Panel Data (DPD) reduced-form model, we apply alternative approaches (fixed and random effects and GMM-based methods) for measuring the induced demand. The results obtained provide evidence for the existence of an induced demand for transport in Spain, though said results vary depending on the estimating method employed.     

Impacts of combining partial and general equilibrium modelling in freight transport analyses – a forest sector case study from Norway

Abstract

The forest sector in Norway is very transport intensive, accounting for approximately 14% of total domestic freight transport traffic on Norwegian roads. This paper presents an analysis linking a general equilibrium freight transport modelling tool with a partial equilibrium model of the forest sector. The freight transport model predicts transport costs, modal split and transport patterns, and the results are treated as inputs to the forest sector model. The objective of the paper is to analyse the modelling effect of taking forest sector model effects back into the freight transport model and treated as new demand. Compared to a base scenario for the year 2020, we compare analyses with and without this new demand from the forest sector model back into the freight transport modelling tool.     

Estimating fuel demand elasticities to evaluate CO2 emissions: Panel data evidence for the Lisbon Metropolitan Area

This paper estimates fuel demand models for the Lisbon Metropolitan Area (AML) and uses the demand elasticities obtained to predict future levels of road transport CO₂ greenhouse gas emissions. Data for the municipalities constituting the AML and the period 1993–2010 are analysed using static and dynamic panel data models to measure the relative importance of fuel price, income, vehicle stock, the price of public transport, and the availability of urban and suburban rail networks on fuel demand. To the best of our knowledge, this is the first study in the Portuguese context to produce fuel demand elasticities for a specific metropolitan area, as opposed to the estimation of country-level aggregate elasticities. Our findings indicate that the elasticity of fuel demand with respect to fuel price ranges between −0.48 and −0.72 in the short run and between −1.19 and −1.82 in the long run. Income elasticities are found to range between 0.51 and 0.54 in the short run and between 1.26 and 1.37 in the long run. The elasticity of fuel demand with respect to vehicle stock (keeping population constant) is 0.57 in the short run and 1.43 in the long run. There is only weak evidence of a reduction in fuel demand as a result of a decrease in the price of public transport, and no effect of greater availability of rail networks. Based on the elasticities estimated, we predict road transport CO₂ emissions for the AML according to different macroeconomic scenarios. The results indicate that the emissions target is only achieved in the scenario of poor economic performance. In the presence of medium and strong economic growth, fuel prices would need to increase by about 7% and 11% per year respectively in order to meet the emissions target.     

Quantifying uncertainties in a national forecasting model

Uncertainties related to demand model system outputs is an important issue in travel demand models. This paper focuses on uncertainties arisen from the fact that models are estimated on a sample of the population (and not the whole population). Forecasting systems can be quite complex, and may contain procedures that not easily permit analytically derived statistical measures of uncertainty. In this paper, the possibilities to use computer-intensive numerical methods to compute statistical measures for very complex systems, without being bound to an analytical approach, are explored. Here, the bootstrap method is used to obtain statistical measures of outputs produced by the forecasting system SAMPERS. The SAMPERS system is used by Swedish transport authorities. The bootstrap method is briefly described as well as the procedure of applying bootstrap on the SAMPERS system. Numerical results are presented for selected forecast results at different levels such as total traffic demand, origin–destination demand, train line demand and the demand on specific links. Also, the uncertainty related to the value of time estimate is analysed.     

Transport Demand Management in Turkey: A Genetic Algorithm Approach

Abstract

This article proposes new models for estimating transport demand using a genetic algorithm (GA) approach. Based on population, gross national product and number of vehicles, four forms of the genetic algorithm transport planning (GATP) model are developed – one exponential and the others taking quadratic forms – and applied to Turkey. The best fit models in terms of minimum total average relative errors in the test period are selected for future estimation. Demand management strategies are proposed based on three scenarios: restricting private car use, restricting truck use and the simultaneous management of private car use and goods movement. Results show that the GATP model may be used to estimate transport demand in terms of passenger-kilometers traveled (pass-km), vehicle-kilometers traveled (veh-km) and ton-kilometers completed (ton-km). Results also show that the third scenario – simultaneous restrictions on private car use and goods movement – could reduce total veh-km by about 35% by 2025 in this study of Turkish rural roads.