Estimating traffic demand risk – A multiscale analysis

This paper proposes a novel method for estimating the traffic demand risk associated with transportation. Using mathematical properties of wavelets, we develop a statistical measure of traffic demand sensitivity with respect to GDP. This measure can be adapted in a flexible way to capture risk levels relevant for different investment horizons. We demonstrate the timescale decomposition of risk with Swedish traffic demand data for 1950–2005. In general, rail transport shows a stronger co-movement with GDP than road transport. Moreover, we examine the volatility exhibited by traffic demand. Our findings suggest that rail investments are more risky than road investments. Since the findings can be used for optimal investment timing and for choice between public investment alternatives, they are deemed important for public policy in general.     

Step-tolling with price-sensitive demand: Why more steps in the toll make the consumer better off

Most dynamic models of congestion pricing use fully time-variant tolls. However, in practice, tolls are uniform over the day, or at most have just a few steps. Such uniform and step tolls have received surprisingly little attention from the literature. Moreover, most models that do study them assume that demand is insensitive to the price. This seems an empirically questionable assumption that, as this paper finds, strongly affects the implications of step tolling for the consumer. In the bottleneck model, first-best tolling has no effect on the generalised price, and thus consumer surplus remains the same as without tolling. Conversely, under price-sensitive demand, step tolling increases the price, making the consumer worse off. The more steps the toll has, the closer it approximates the first-best toll, thereby increasing the welfare gain and making consumers better off. This indicates the importance for real-world tolls to have as many steps as possible: this not only raises welfare, but may also increase the political acceptability of the scheme by making consumers better off.     

An analysis of destination choice for opaque airline products using multidimensional binary logit models

We investigate how customers respond to an opaque airline product offered by a European carrier. In this opaque product design, customers are randomly assigned to travel to one of approximately ten destinations; however, for a fee they may exclude one or more destinations from the choice set (or a particular package design) prior to learning which destination they will travel to. We use a multidimensional binary logit model to predict the probability that one or more alternatives will be chosen by a customer. Results show that customers are more likely to pay to exclude destinations located close to the origin airport and destinations that speak the same language as the origin airport. Length of stay, cost of living at the destination, and measures of destination attractiveness are also found to be significant predictors for some package designs. Based on these findings, we offer general recommendations for how to design opaque packages for airline customers.     

To kill a real option – Incomplete contracts,real options and PPP

This paper is concerned with the implications of public–private partnership agreements for the execution of expansion options in road infrastructure. More specifically, it analyzes the expansion of an existing two-lane road in Sweden, and examines the real options created by an intermediate type of road with three lanes. Interpreting the results from real option analysis in the light of incomplete contract theory, this paper finds that external congestion costs might necessitate public ownership to ensure a social optimal outcome in public–private partnerships.     

Home-to-work commuting,urban form and potential energy savings: A local scale approach to regional statistics

The link between transport energy consumption and land use patterns has been the focus of a considerable amount of academic works over the past decades. While many empirical researches are backed up with solid statistical techniques, most of them do not fully consider the influence of scale underlying empirical quantitative investigations. Using fine-scale home-to-work commuting data for Wallonia (Belgium), this paper re-evaluates Breheny’s (1995) assertion that urban structure should hold the characteristics of major cities if substantial energy savings are to be achieved. A local scale approach highlights efficient settlements in terms of transport energy consumption not only within major towns, but also within remote rural areas. Furthermore, results suggest that influencing the urban form following local energy efficient examples rather than regional ones could also yield significant gains, without an extreme policy stance of re-urbanisation in major cities.     

Remoteness and accessibility in the vulnerability analysis of regional road networks

This paper considers the development of a method for network vulnerability analysis which considers the socio-economic impacts of network degradation and seeks to determine the most critical locations in the network. The method compares the levels of remoteness (or its inverse, accessibility) of localities within the study region, on the basis of the impacts of degradation of the road network on a recognised accessibility/remoteness index that can be applied to each and every location within the region. It thus extends the earlier work on accessibility-based vulnerability analysis which was limited to assessment of impacts on selected nodes in a network. The new method allows study of impacts on both specified locations (which do not have to be represented as network nodes) and the region as a whole. The accessibility/remoteness index is defined so that an accessibility surface can be calculated for the region, and the volume under this surface provides an overall measure of accessibility. Changes in the volume under different network states thus reflect the overall impacts. The method is applied to a rural region in south east Australia.     

Analysis of the effectiveness of control measures to mitigate road dust emissions in a regional network

The effectiveness of control measures to reduce road dust emissions is analyzed using a year’s data of road dust emissions collected with a mobile sampling platform and a survey of road maintenance practices in the Lake Tahoe Basin of Nevada and California US. Attributes such as sweeping practices, anti-icing, shoulder improvement, pavement condition, trackout, and abrasive material from road segments were analyzed with a feature subset selection algorithm. Street sweeping was found to be an effective means of controlling dust emissions from roads. Road dust from dirty tertiary roads served as a continuous source of suspendable material for adjacent high-speed roads in the winter time. To be most effective, emission control strategies require that not only primary roads, but all roads be swept after snow storms to recover applied abrasive material.     

Influence of vehicle driving parameters on the noise caused by passenger cars in urban traffic

In this work, a sample of vehicles has been instrumented to measure of variables that influence vehicle noise emissions in Madrid. A circuit reproducing a normal travel pattern in large city is traveled by a fleet of vehicle models representing the fleets of cars in a European city. A sample of drivers covers the test track under different traffic conditions. Driving parameters and noise emitted have been recorded in each test and average values have been extracted. These data have been analyzed to define the noise emissions produced by a vehicle in real driving conditions and to identify the noisiest driving behaviors.     

Delivering mobility as a service (MaaS) through a broker/aggregator business model

Transportation - Mobility as a service (MaaS) promises a bold new future where bundled public transport and shared mobility options (carsharing, ridesharing, bikesharing and microtransit) will...     

Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle’s mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the Biogeography-Based Optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.