Estimation of road profile variability from measured vehicle responses

When assessing the statistical variability of fatigue loads acting throughout the life of a vehicle, the question of the variability of road roughness naturally arises, as both quantities are strongly related. For car manufacturers, gathering information on the environment in which vehicles evolve is a long and costly but necessary process to adapt their products to durability requirements. In the present paper, a data processing algorithm is proposed in order to estimate the road profiles covered by a given vehicle, from the dynamic responses measured on this vehicle. The algorithm based on Kalman filtering theory aims at solving a so-called inverse problem, in a stochastic framework. It is validated using experimental data obtained from simulations and real measurements. The proposed method is subsequently applied to extract valuable statistical information on road roughness from an existing load characterisation campaign carried out by Renault within one of its markets.     

Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

Travel time reliability, an essential factor in traveler route and departure time decisions, serves as an important quality of service measure for dynamic transportation systems. This article investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. A volume-to-capacity ratio-based travel time function and a point queue model are used to demonstrate how day-to-day travel time variability can be explained from the underlying demand and capacity variations. One important finding is that closed-form solutions can be derived to formulate travel time variations as a function of random demand/capacity distributions, but there are certain cases in which a closed-form expression does not exist and numerical approximation methods are required. This article also uses probabilistic capacity reduction information to estimate time-dependent travel time variability distributions under conditions of non-recurring traffic congestion. The proposed models provide theoretically rigorous and practically useful tools for understanding the causes of travel time unreliability and evaluating the system-wide benefit of reducing demand and capacity variability.     

Decision support for risk assessment of mid-air collisions via population-based measures

Specifying proximity warning functions for aircraft in managed airspace has received considerable attention. However, similar functions for aircraft operating in unmanaged airspace have received comparatively little analysis despite the fact that these functions are stressed to a greater physical degree, and perhaps more frequently, than in managed airspace. The mid-air collision hazard and its associated risk are re-examined from both an historical and a systematic engineering modelling viewpoint. Historic measures of this transport risk in managed airspace have been based on fatalities normalized by flight hours or flight movements. However some of these data may not be available in unmanaged airspace. Another approach to measurement directs attention to populations at risk where several measures are now well known: collective risk, individual risk and the frequency of occurrence of the hazards that give rise to such risk. A decision support methodology is presented that relates both transport and population-based approaches. A cohesive and consistent set of aspired goals for various stakeholder groups can be set taking into account the different stakeholder needs. A case study is drawn from historic mid-air collision data to illustrate the process. A consistent basis for national-level policy decisions harmonised with proactive engineering design requirements is achieved. The strengths, limitations and implications of this approach for engineering design purposes are discussed.     

Large oil tankers and the Baltic marine environment: A statistical survey

The short Baltic export route for Russian oil is an attractive source of supply for the European energy market. The article examines the Russian oil export trade in the Baltic based on Automatic Identification System (AIS) data from 2005 matched with supplementary data. The first part of the article gives a survey of activities and characteristics of 261 large crude oil tankers, their flag states and owner countries. The second part views the data in the context of accidental oil spill risk. Greek and Russian owners are leading carriers in this trade. Russia plays a double leading role as oil exporter and shipowning country, securing major cargo shares for nationally owned vessels, while also giving a preferential spot in the trade to Greek owners. Where environmental aspects are concerned, previous research has linked vessel characteristics with the risk of casualty. Knowledge of flag, age, ownership and activity levels adds useful information about environmental risk. The data show that flags associated with very high casualty or detention probabilities were represented by 18% of the activity in this trade. Older vessels have a similar share, and at this particular time probably represent the more serious threat of accidental oil spills.     

Blast loaded plates

Plates form one of the basic elements of structures. Land-based structures may be subjected to air blast loads during combat environment or terrorist attack, while marine structures may be subjected to either air blast by the attack of a missile above the water surface or an underwater explosion by the attack of a torpedo or a mine or a depth charge and an aircraft structure may be subjected to an in-flight attack by on-board explosive devices. Furthermore, gas explosion occurs in offshore installations and industries. This review focuses on the phenomenological evolution of blast damage of plates.     

Vehicle sensor data-based analysis on the driving style differences between operating indoor simulator and on-road instrumented vehicle

Abstract

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving performance could be different in the real world and in the simulated world. Even though many studies have been conducted to discover the differences of driving behaviors in these two circumstances, little research has focused on analyzing the differences in driving style, which can provide more integrated knowledge of a driver from the natural structure, stimulus–response mechanism, of driving behaviors. Therefore, in this paper, the driving styles in both the real world and the simulated world are extracted by implementing the nonnegative matrix factorization method on the collected VSD data. Through this analysis, the driving style differences can be quantitatively described and discussed in detail. It is found that the drivers tend to be more unstable and sometimes aggressive when driving the simulator and the deviation in the perception of temporal gap in two circumstances is also discovered. The research findings are particularly valuable to calibrate the driving simulator and construct more reliable driving behavior models.     

Passenger waiting time and information acquisition using automatic vehicle location for verification

Bus riders utilize a variety of information media to learn how to travel to their destinations and to learn when they should arrive at bus stops. As part of the OCTA (Orange County Transit Authority) Transit Probe evaluation, 1199 passengers were surveyed to measure relationships between information acquisition and waiting time. A unique aspect of the survey was that some of the data could be correlated with automatic‐vehicle‐location (AVL) measurements of bus lateness at stops. Insights are provided as to the types of information riders acquire based on the nature of the trip and demographic characteristics. Insights are also provided as to factors affecting perceived waiting time. We found age group, whether a person needs to arrive at a destination by a specific time, primary language, and whether the person is a first‐time user of the bus line to be significant causal factors.     

The social context of informal commuting: Slugs, strangers and structuration

Despite considerable interest in the role of social interactions and social context on transportation, there have been very few attempts to explore specific cases of social interaction influencing transportation systems. This paper explores the social practice of slugging, an informal system of carpooling in the Washington, DC area. Slugging emerged in response to the establishment of Virginia’s High Occupancy Vehicle (HOV) lanes in the early 1970s, as single drivers picked up riders alongside the road (slugs) in order to meet the requirements for driving in the less congested HOV lanes. Drawing on the work of sociologist Anthony Giddens, as well as the sociological insights of Georg Simmel and Stanley Milgram, we suggest that the practice of slugging highlights the processes of institutionalization and structuration. This paper details how the region’s mass transportation policies and urban culture have combined to result in an institutionalized practice with particular norms and logics of behavior. We conclude that looking at specific cases where social context has affected transportation, like slugging, could provide useful insights on the impact of social context on transportation policies and systems.     

Strategies to achieve deep reductions in metropolitan transportation GHG emissions: the case of Philadelphia

ABSTRACT

This paper investigates strategies that could achieve an 80% reduction in transportation emissions from current levels by 2050 in the City of Philadelphia. The baseline daily lifecycle emissions generated by road transportation in the Greater Philadelphia Region in 2012 were quantified using trip information from the 2012 Household Travel Survey (HTS). Emissions were projected to the year 2050 accounting for population growth and trends in vehicle technology for both the Greater Philadelphia Region and the City of Philadelphia. The impacts of vehicle technology and shifts in travel modes on greenhouse gas (GHG) emissions in 2050 were quantified using a scenario approach. The analysis of 12 different scenarios suggests that 80% reduction in emissions is technically feasible through a combination of active transportation, cleaner fuels for public transit vehicles, and a significant market penetration of battery-electric vehicles. The additional electricity demand associated with greater use of electric vehicles could amount to 10.8 TWh/year. The use of plug-in hybrid electric vehicles (PHEV) shows promising results due to high reductions in GHG emissions at a potentially manageable cost.