Multiple objective optimization of the fleet sizing problem for road freight transportation

A fleet sizing problem (FSP) in a road freight transportation company with heterogeneous fleet and its own technical back‐up facilities is considered in the paper. The mathematical model of the decision problem is formulated in terms of multiple objective mathematical programming based on queuing theory. Technical and economical criteria as well as interests of different stakeholders are taken into account in the problem formulation. The solution procedure is composed of two steps. In the first one a sample of Pareto‐optimal solutions is generated by an original program called MEGROS. In the second step this set is reviewed and evaluated, according to the Decision Maker's (DM's) model of preferences. The evaluation of solutions is carried out with an application of an interactive multiple criteria analysis method, called Light Beam Search (LBS). Finally, the DM selects the most desirable, compromise solution.     

Planning for cycling in the dispersed city: establishing a hierarchy of effectiveness of municipal cycling policies

Transportation - Urban utility cycling is being promoted widely due to various health, social, economic and environmental benefits. This study seeks to identify and rank which municipal-level...     

Map-matching poor-quality GPS data in urban environments: the pgMapMatch package

Global Positioning System (GPS) data have become ubiquitous in many areas of transportation planning and research. The usefulness of GPS data often depends on the points being matched to the true sequence of edges on the underlying street network – a process known as ‘map matching.’ This paper presents a new map-matching algorithm that is designed for use with poor-quality GPS traces in urban environments, where drivers may circle for parking and GPS quality may be affected by underground parking and tall buildings. The paper is accompanied by open-source Python code that is designed to work with a PostGIS spatial database. In a test dataset that includes many poor-quality traces, our new algorithm accurately matches about one-third more traces than a widely available alternative. Our algorithm also provides a ‘match score’ that evaluates the likelihood that the match for an individual trace is correct, reducing the need for manual inspection.     

A randomized controlled trial in travel demand management

Transportation - This paper presents a trial aimed at reducing parking demand at a large urban employer through an informational campaign and monetary incentives. A 6-week randomized controlled...     

Identification of occupant posture using a Bayesian classification methodology to reduce the risk of injury in a collision

Recent studies have shown that smart restraint systems, which will recognize and then adapt to a specific collision and occupant combination, have a strong opportunity to significantly reduce occupant injuries during a traffic accident. As a step toward the development of these adaptive restraint systems, this study proposes a novel methodology for the classification of pre-crash occupant posture. Various occupant postures were simulated with a human model and the corresponding data was recorded using sensor models implemented in a mid-size car interior. The sensor data was then used to train two Bayesian classifiers which categorized an unknown occupant posture as one of nine predefined classes. The posture classifiers and a look-up table which contained optimized restraint laws for each class were combined to form catalog controllers for the restraint systems. The benefit of these restraint systems with catalog controllers vs. a restraint system optimized at a nominal posture was estimated by analyzing crash simulations with the occupant in 200 different postures. While the minimum error rate classifier showed the highest correct classification rate (90%), the Bayesian minimum risk classifier estimated the highest average injury reduction (21%). As expected, the highest injury reduction (up to 45%) was recorded for the posture classes closest to the windshield, whereas the lowest injury reduction was found for the classes closest to the nominal position. While the proposed restraint system with a catalog controller requires considerable “offline” computational effort, it is more versatile in terms of using complex human models and injury criteria and is much faster during the brief decision window available than recent “online” controllers proposed previously in literature.     

Economic resilience to transportation failure: a computable general equilibrium analysis

This study develops and applies a multimodal computable general equilibrium (CGE) framework to investigate the role of resilience in the economic consequences of transportation system failures. Vulnerability and economic resilience of different modes of transportation infrastructure, including air, road, rail, water and local transit, are assessed using a CGE model that incorporates various resilience tactics including modal substitution, trip conservation, excess capacity, relocation/rerouting, and service recapture. The linkages between accessibility, vulnerability, and resilience are analyzed. The model is applied to the transportation system failures in the aftermath of Hurricane Katrina to illustrate its capabilities. The analytical framework, however, has broader applications and can provide insights for resource allocations to enhance emergent responses to unexpected events and to improve resilient design of transportation infrastructure systems.     

Exploring stability and change in transport systems: combining Delphi and system dynamics approaches

Transport is a vast and complex socio-technical system, and despite a clear need to reduce dependence on fossil fuels due to undesirable environmental impacts, it is largely locked into business-as-usual. Systems approaches are a useful way to help make sense of multiple competing influences which may be simultaneously driving change and supporting the status quo. This paper applies qualitative system dynamics modelling to help interpret the results of a Delphi study into global transport transitions, involving 22 international experts in various aspects of transport. The main contribution of the paper is its exploration of the use of system dynamics (SD) modelling to interpret the Delphi findings. SD modelling was used to reveal and elucidate the causal arguments put forward by the expert panel about the factors driving business-as-usual, the factors creating barriers to more sustainable transport systems, and the drivers of change. The SD model is used to explore and expose the key causal patterns at play, and how these interact to both support and hinder change. The resulting model shows the complex, interdependent dynamics involved in supporting the status quo. Even at the relatively high level of analysis reported here, the model is useful in revealing interdependencies between parts of the system, where change in one part may well have knock-on effects elsewhere in the system. In particular the model reveals the strong reinforcing loops that act to minimise the impact of change drivers and thus retain the dominance of automobility. The result is a system that is highly dependent on the continued existence of key reinforcements such as policies that subsidise fossil fuels. From a methodological perspective, the outcomes of the Delphi study provided a rich source of qualitative material which was highly suitable for developing a system dynamics model.     

How commuting affects subjective wellbeing

Transportation - Commuting between home and work is routinely performed by workers and any wellbeing impacts of commuting will consequently affect a large proportion of the population. This paper...