The impact of road pricing on housing prices: Preliminary evidence from Milan

Road pricing is increasingly used as an economic tool to decrease the burden of transport externalities. Following the examples of several cities worldwide, on 2nd January, 2008, the city of Milan introduced a charge for accessing the city centre with the aim of curbing air pollution and congestion. The aim of this paper is to evaluate empirically the effect of such a charge on the housing market. By applying a difference-in-differences methodology, I find empirical support for a decrease in housing prices in the coverage area.     

The environmental impact caused by road access to Timanfaya Natural Park on Lanzarote Island

This paper evaluates the impact of road transport usage in accessing the Timanfaya Natural Park on Lanzarote Island, and its implications for sustainable tourism development. This evaluation is based on projections of the trip matrix estimated for Lanzarote Island. First, we estimate the global environmental impact, or the contribution of the road transport required for accessing the natural park to climate change. The methodology employed is the Ecological Footprint indicator. The study analyses how to manage the impact of road access to tourist activities through a price intervention; i.e. fuel tax. Redesigning the public transport supply is proposed as an alternative to the pricing policy. Finally, conclusions are drawn.     

The effect of non-recreational transport cycling on use of other transport modes: A cross-sectional on-line survey

Accurate modelling of the health and environmental benefits of non-recreational transport cycling requires information about its effects on the use of other transport modes. Relevant research has not focussed on cycling for transport in a general context (as opposed to bikeshare), nor allowed for multi-modal trips. The influence of trip- and personal-characteristics on whether cycling replaces car-driving have yet to be considered. The present study aimed to address these research gaps. An on-line cross-sectional survey was completed by 1525 Australians who cycle for transport at least once per week. For the most recent trip completed (at least partly) by bicycle participants provided trip distance, and percentage travelled by car, public transport (PT), and walking. They also provided the percentage travelled by each mode for the same trip before taking up transport cycling; and a hypothetical future trip when riding is not possible. Compared to the same trip before, fifty percent of recent trips reduced car use, and around 1/3 eliminated a 100%-car trip. Reduced car use was significantly less likely for trips under 7.5 km, commuting, females, respondents under 55, and regular cyclists. Reduced car use was less likely for respondents who started riding because it is flexible, and more likely for those who started riding to avoid parking. Car-use was reduced by an average of 6.2 km per trip, and each bicycle-km cycled replaced 0.5 car-km. Participants report that since taking up cycling, even when they cannot use their bike, they use cars less and use PT more compared to before they took up cycling. Results suggest that previous studies underestimated the extent to which transport cycling replaces car travel, and highlights trip types and population groups to target with cycling promotion strategies. Information about the per-trip and per-bicycle-km replacement of car, PT and walking may be used for more accurate estimation of the benefits of transport cycling than has hitherto been possible.     

A spatial autoregressive panel model to analyze road network spillovers on production

The production function approach is used to introduce the effect of public infrastructure on economic growth focusing on its spillover effects. We improve the existing literature both from a conceptual and methodological perspective. As regressors we incorporate variables related to the new concepts of internal and imported transport infrastructure capital stocks, which are actually used in commercial flows, calculated by network analysis performed in GIS. The internally used capital stock represents own infrastructure that benefits accessing markets within the region itself, while the imported capital stock captures the spillover effect associated to the use of the infrastructure situated in neighboring regions. From a methodological perspective, we introduce spatial interdependence into these models, applying the most recent spatial econometric techniques based on instrumental variables estimation in spatial autoregressive panel models in comparison with Maximum Likelihood estimation methods. We illustrate the methodology with Spanish provincial panel data for the period 1980–2007. Results support the hypothesis that the imported capital has a positive spillover effect on production.     

A bi-objective bi-level signal control policy for transport of hazardous materials in urban road networks

A bi-objective bi-level signal control optimization for hazardous material (hazmat) transport is considered to assess trade-offs between travel cost and environment impacts such as public risk exposure. A least maxi-sum risk model with explicit signal delay is presented to determine generalized travel cost for hazmat carriers. Since the bi-level signal control problem is generally a non-convex program, a bundle method using generalized gradients is proposed. A bounding strategy is developed to stabilize solutions of the bi-level program and reduce relative gaps between iterations. Numerical comparisons are made with other risk-averse models. The results indicate that the proposed bi-objective bi-level model becomes even amiable to signal control policy makers since provides flexible solutions whilst is acceptable to carriers since takes account of travel delay at signal-controlled junctions. Moreover, the trade-offs between public risk and generalized travel costs are empirically investigated among different risk models with a variety of weights. As a result, the proposed model consistently exhibits highly considerable advantage on mitigation of public risk whilst incurred less cost loss as compared to other alternatives.     

The impact of road traffic noise on urban protected areas: A landscape modeling approach

We modeled the propagation of traffic noise over the landscape and analyzed its impact on the structure and configuration of protected areas of the Twin Cities Metro Region, Minnesota. Using four noise thresholds, we found that at low and medium noise levels, 19% and 11% of the protected areas are within the road-effect zone. Using mean patch area and patch shape index, we measured the acoustic fragmentation of habitats. We found that at higher levels of noise patch shape index increased, while mean patch size decreased. The acoustic diversity of a patch is also found to be correlated with land cover type, patch area, and patch shape.     

Integrated approach to determine highway flooding and critical points of drainage

Among the natural hazards that threaten transportation infrastructure, flooding represents a major hazard to highways as it challenges their design, operation, efficiency and safety. In extreme cases, it may lead to massive obstruction of traffic and direct damages to the road structures themselves and indirect damages to the economic activity and development of the region. To enable the prevention of such consequences, and the proposition of adaptive measures for existing infrastructure, this paper presents an integrated framework to identify the most vulnerable points to flooding along a highway. This is done through the combination of remote sensing information (e.g. LiDAR based Digital Elevation Model, satellite imagery), a high-quality dataset, and a quasi-2D hydrodynamic model. The forcing condition is defined using a hyetograph associated to a storm with duration of 1 day and return period of 100 years. The selected highway is located in the Mexican state of Tabasco, where extreme precipitation events and floods are frequent. Results demonstrate the ability of the methodology to identify critical water levels along the road (h > 1.50 m) at those locations where flooding has been experienced, as well as points of inspection for the highway drainage. These locations were visited in the field and maintenance problems were detected that do increase its level of exposure. We show that this framework is useful for the generation of a flood management strategy to the analyzed highway, which includes an optimum location of adaptive measures to an anticipated more intense future climate.     

The applicability of non-cooperative game theory in transport analysis

Various models that incorporate concepts from Non-Cooperative Game Theory (NCGT) are described in the transport literature. Game Theory provides powerful tools for analysing transport systems, but these tools have some drawbacks that should be recognised. In the current paper we review games that describe transport problems and discuss them within a uniform context. Although the paper does not introduce new tools, it presents insights concerning the relations between transport models and games. We divide existing games into groups and show that some common features characterise multiple games. We distinguish between games that make a conceptual contribution and games that are suitable for application. Compact or symmetric game structures make remarkable observations but often do not support actual decision-making. Less aesthetic formats, most of which are Stackelberg games between authorities and travellers, are stronger as instruments that assist in determining real-life policies; these formulations can be treated by practitioners as mathematical programs with equilibrium constraints and not as games. Yaron Hollander is currently conducting economic research of bus reliability at the Institute for Transport Studies at the University of Leeds. He previously worked for the Technion—Israel Institute of Technology; for the Israel Institute for Transportation Planning and Research; and for the public transport department at Ayalon Highways Co. Joseph N. Prashker is a professor at the Faculty of Civil and Environmental Engineering at the Technion—Israel Institute of Technology. Till recently he served as head of the Transportation Research Institute at the Technion. His interests are behavioural demand models, network analysis, and Game Theory applications in transportation.     

Impact assessment of extreme weather events on transport networks: A data-driven approach

The present paper presents a data-driven method for assessing the resilience of the European passenger transport network during extreme weather events. The method aims to fill in the gap of current research efforts regarding the quantification of impacts attributed to climate change and the identification of substitutability opportunities between transport modes in case of extreme weather events (EWE). The proposed method consists of three steps concerning the probability estimation of an EWE occurring within a transportation network, the assessment of its impacts and the passengers’ flow shift between various transport modes. A mathematical formulation for the proposed data-driven method is provided and applied in an indicative European small-scale network, in order to assess the impacts of EWE on modal choice. Results are expressed in passenger differentiated flows and the paper concludes with future research steps and directions.     

Transit network design with allocation of green vehicles: A genetic algorithm approach

The use of fossil fuels in transportation generates harmful emissions that accounts for nearly half of the total pollutants in urban areas. Dealing with this issue, local authorities are dedicating specific efforts to seize the opportunity offered by new fuels and technological innovations in achieving a cleaner urban mobility. In fact, authorities are improving environmental performances of their public transport fleet by procuring cleaner vehicles, usually called low and zero emission vehicles (LEV and ZEV, respectively). Nevertheless there seems to be a lack of methodologies for supporting stakeholders in decisions related to the introduction of green vehicles, whose allocation should be performed since the network design process in order to optimize their available green capacity.In this paper, the problem of clean vehicle allocation in an existing public fleet is faced by introducing a method for solving the transit network design problem in a multimodal, demand elastic urban context dealing with the impacts deriving from transportation emissions.The solving procedure consists of a set of heuristics which includes a routine for route generation and a genetic algorithm for finding a sub-optimal set of routes with the associated frequencies.     

Quantifying the climate impact of emissions from land-based transport in Germany

Although climate change is a global problem, specific mitigation measures are frequently applied on regional or national scales only. This is the case in particular for measures to reduce the emissions of land-based transport, which is largely characterized by regional or national systems with independent infrastructure, organization, and regulation. The climate perturbations caused by regional transport emissions are small compared to those resulting from global emissions. Consequently, they can be smaller than the detection limits in global three-dimensional chemistry-climate model simulations, hampering the evaluation of the climate benefit of mitigation strategies. Hence, we developed a new approach to solve this problem. The approach is based on a combination of a detailed three-dimensional global chemistry-climate model system, aerosol-climate response functions, and a zero-dimensional climate response model. For demonstration purposes, the approach was applied to results from a transport and emission modeling suite, which was designed to quantify the present-day and possible future transport activities in Germany and the resulting emissions. The results show that, in a baseline scenario, German transport emissions result in an increase in global mean surface temperature of the order of 0.01 K during the 21st century. This effect is dominated by the CO₂ emissions, in contrast to the impact of global transport emissions, where non-CO₂ species make a larger relative contribution to transport-induced climate change than in the case of German emissions. Our new approach is ready for operational use to evaluate the climate benefit of mitigation strategies to reduce the impact of transport emissions.     

暑运:第四份大餐

近年来,每到暑假,人们外出旅游和大学生返家返校人数越来越多.据铁道部全国铁路暑期运输方案预测:今年全国铁路暑期(自7月1日起至8月31日止,共62天)将发送旅客2.21亿人次,同比增加858万人次,增长4%左右.而2006年40天创历史最高纪录的春运,铁路发送旅客仅为1.49亿人.可见暑运已经成为又一个黄金季节,理当成为道路客运企业一年中的第四份大餐.     

Simulating deep CO2 emission reduction in transport in a general equilibrium framework: The GEM-E3T model

Transport sector restructuring to achieve deep GHG emission cuts has attracted much attention because transportation is important for the economy and inflexible in greenhouse gas emission reduction. The aim of this paper is to simulate transition towards low carbon transportation in the European Union until 2050 and to assess the ensuing macroeconomic and sectorial impacts. Transport restructuring is dynamically simulated using a new transport-oriented version of the computable general equilibrium model GEM-E3 which is linked with the PRIMES-TREMOVE energy and transport sectors model. The analysis draws from comparing a reference scenario projection for the EU member-states up to 2050 to alternative transport policy scenarios and sensitivities which involve deep cutting of CO₂ emissions. The simulations show that transport restructuring affects the economy through multiple channels, including investment in infrastructure, the purchasing and manufacturing of new technology vehicles, the production of alternative fuels, such as biofuels and electricity. The analysis identifies positive impacts of industrial activity and other sectors stemming from these activities. However, the implied costs of freight and passenger transportation are of crucial importance for the net impact on GDP and income. Should the transport sector transformation imply high unit costs of transport services, crowding out effects in the economy can offset the benefits. This implies that the technology and productivity progress assumptions can be decisive for the sign of GDP impacts. A robust conclusion is that the transport sector decarbonisation, is likely to have only small negative impacts on the EU GDP compared to business as usual.     

Travel,transport and energy implications of university-related student travel: A case study approach

This study highlighted significant cultural differences and complexity in travel behaviour associated with travel to university across the UK and Ireland. This paper examines university travel behaviours and the implications for emissions, across the 2012–2013 academic year, based on responses from 1049 students across 17 universities in Ireland and the UK. Surveys were analysed to examine the trips of students both during term time and when accessing the universities each year. The data analysis in this paper examines three aspects of the transport implications of travel to and from university. Firstly the journey between university and term time address (or permanent address if the respondent does not have a separate term time address), secondly the journey between the university area and a separate permanent address where relevant; and thirdly implications for emissions resulting from university-related travel.The study found that student car users were more likely to be female, older students, or studying part time; male students were more likely to use active modes. The study indicated interesting differences between students living in different parts of the UK and Ireland. For example, it was found that there was a higher level of car dependence amongst Northern Irish students compared to other areas; and a greater variability in travel distances in Scotland and Northern Ireland. In England, car use was more pronounced when students travelled from their permanent address to term time address, and, as in Ireland, there was evidence of more car sharing on such trips. Public transport usage was more pronounced amongst Scottish students. The effect of these transport choices on emissions is significant and demonstrates the importance of education related trips to the development of a transport policy response. The analysis shows that annual emissions are highest for regular travel to and from university when a student has a permanent address rather than a separate term time and permanent address.     

A multi-layer scenario analysis for sustainable international transport

This article considers the development of the international transport sector based on four globalization scenarios. These four images of the future transportation market are constructed at three different levels (global, European and Dutch). The possible consequences of these scenarios are mapped out not only by key aspects such as modal split and spatial organization but also by providing empirical insights into expected transport flows for both passenger and freight transport in 2020 based on data from 1995.     

Improvement in the estimation and back-extrapolation of CO2 emissions from the Irish road transport sector using a bottom-up data modelling approach

Road transport is a major source of CO₂ emissions in Ireland and accounts for almost 96% of the total CO₂ emissions from the transport sector. Following the recent adopted UNFCCC reporting guidelines on annual inventories [24/CP.19], this study applied the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006 IPCC GLs) tier 3 approach to estimate CO₂ emissions from road transport at the vehicle category level, for the first time in Ireland. For this, disaggregated datasets were prepared based on year of vehicle registration and mileage since registration of the vehicle. Such an approach provided a more realistic national scenario in comparison to the use of average mileage degradation in emission calculations. This investigation comprised a recalculation of previous emissions estimates (1990–2012) and an estimation of CO₂ emissions in 2013 using a previously unavailable level of data disaggregation for vehicle mileage as well as using vehicle class specific data and an improved bottom-up estimation methodology in COPERT. Historic vehicle fleet data were restructured, annual mileage data were estimated in relation to the fleet data and back extrapolated using a regression approach.The results showed that the mileage degradation was not only subject to fuel technology, engine size, and age but also the emissions class and vehicle category. It was also observed that the disaggregated level of data provided a different CO₂ emissions split among the vehicle categories than that of previous estimations which were based on an aggregated level of data. Previous emissions inventories (1990–2012) were shown to have underestimated the share from diesel fuelled passenger cars by more than 56% in 2012. Diesel fuelled passenger cars were also found to account for the majority of CO₂ emissions from road transport activities in Ireland in 2013. The level and trend assessment showed that emissions from Euro-II and Euro-III classed vehicles especially for passenger cars, which have a significant contribution to the total emission in 2013 have caused an increase in fleet level emissions in Ireland. In addition, the results also showed that the emissions share from Light Duty Vehicles and Heavy Duty Vehicles were overestimated by previous investigations. This paper highlights the importance of the resolution of data used in emissions inventory preparation which may impact upon future projections and policy formulation. The findings of this investigation are also discussed in relation their implications for road transport policy, including carbon taxation and future policy options aimed at achieving EU emissions target in 2020.     

Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM₁₀) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM₁₀ concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM₁₀ at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM₁₀ concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM₁₀, gaseous pollutants and meteorological variables respectively.     

The development of a policy for road tax in Turkey, using a genetic algorithm approach for demand estimation

This study deals, first, with estimation of transport demand based on Genetic Algorithm (GA) approach, and then deals with the evaluation of the road tax system in Turkey. It proposes an alternative road tax policy. The total transport demand is estimated based on population, Gross Domestic Product per Capita (GDPPC), and vehicle-number. Three forms of the Genetic Algorithm Transport Demand Estimation for Tax Revenues (GATDETR) are developed, of which one is linear, and the second and third are exponential forms of the mathematical expressions. The best-fit GATDETR model in terms of total minimum relative average errors between observed and estimated values are selected for future demand estimation. The evaluation of the road tax system and policy proposal is made based on estimated demand. The Distance-Based-Taxation (DBT) system is proposed in order to control highway transport. With the DBT system, some road users may wish to use railway. Thus, we re-organize the railways in order to meet the demand, but this requires new fund. The DBT system may help to create to this fund. It may also help to develop fair-taxation for the road users. Results show that the GA can be used to model transport demand and hence income tax in future transports planning. This study also suggests that planning the taxation in highway transport may help to ease funding problem of railway system.     

Optimization of transport investment and pricing policies: the role of transport pricing in network design

This paper investigates the role of transport pricing in network design and describes two facts about flow pattern in a transportation system. The first, illustrated by an example of Braess paradox, is that adding a new link to the network does not necessarily minimize the total travel time. The second is that introducing of appropriate toll pricing may reduce not only the total network time but also the travel time for each individual traveller. It follows with the investigations of different system objectives and different pricing policies (only toll pricing and distance‐based pricing are considered), and shows how they affect the system performance and flow pattern. Lastly, a systematic optimization process is proposed for integrated planning of transport network and pricing policies.