Development of railway station choice models to improve the representation of station catchments in rail demand models

ABSTRACT

This paper describes the development of railway station choice models suitable for defining probabilistic station catchments. These catchments can then be incorporated into the aggregate demand models typically used to forecast demand for new rail stations. Revealed preference passenger survey data obtained from the Welsh and Scottish Governments was used for model calibration. Techniques were developed to identify trip origins and destinations from incomplete address information and to automatically validate reported trips. A bespoke trip planner was used to derive mode-specific station access variables and train leg measures. The results from a number of multinomial logit and random parameter (mixed) logit models are presented and their predictive performance assessed. The models were found to have substantially superior predictive accuracy compared to the base model (which assumes the nearest station has a probability of one), indicating that their incorporation into passenger demand forecasting methods has the potential to significantly improve model predictive performance.     

Back-Calculation of Ground Stress Field and Rockburst Analysis for Wunvfeng Tunnel北大核心CSCD

Studying the in-situ stress distribution at a tunnel site is very important to determine surrounding rock characteristics, the engineering design and the construction scheme. By using the multiple linear regression method based on the least square algorithm, the initial geostress field is analyzed and the corresponding regression coefficients are obtained. The ground stress obtained from the proposed back analysis is reasonable and can meet the demands of the engineering applications. From the rockburst risk level distribution diagram, it is speculated that the Wunvfeng tunnel is in the high filed stress area. Field monitoring should be strengthened and emergency plans should be made to cope with the rockburst risks during the construction process. © 2018, Editorial Office of "Modern Tunnelling Technology". All right reserved.     

Effect of Seismic Parameters on the Safety Factor for an Underground Cavern北大核心CSCD

In order to quantitatively evaluate the safety of the surrounding rock of an underground cavern under seis-mic load, a comprehensive evaluation method for the stability of surrounding rock is proposed based on the general safety factor and point safety factor. A calculation method for the general safety factor of a cavern based on the prin-ciple of shear strength reduction of a rock mass is given, the run-through of the plastic zone between the main power-house and main transformer room is presented as a critical criterion for the overall instability of the cavern, and the general safety factor is obtained by searching for the reduction coefficient. A point safety factor calculation method based on the Mohr-Coulomb yield criterion is given. The influence of different seismic input parameters on the general safety factor of the cavern and the point safety factor of key positions are studied based on an underground power-house cavern of a hydropower station in Southwest China. The results show that the quantitative evaluation method for the stability of the surrounding rock based on the safety factor is feasible and can reflect the general safety de-gree and local safety degree of different positions of the cavern for different working conditions. It is found that the general safety factor of the cavern and the point safety factor of key positions decrease with an increase of the ampli-tude and duration of a seismic wave while they increase with an increase of the incident angle; additionally, the low frequency of a seismic wave has a great influence on the cavern while the high frequency has little effect. © 2018, Editorial Office of "Modern Tunnelling Technology". All right reserved.     

Risk assessment framework for exposure of cargo and ports to natural hazards and climate extremes

There is an increase in risks and catastrophic losses in maritime transport including ports and cargo. Significant losses have been associated with large scale natural hazards, such as earthquakes, tsunami, cyclones, and other extreme weather events. This paper identifies the main gaps in understanding maritime risks in transportation research. The gaps are attributed to insufficient empirical work available from the maritime transport and logistics research community to guide multi-risk and natural hazards impact assessment on seaport and cargo. In addition, disaster studies communities have barely made adequate efforts to understand and assess port and cargo risks arising from multi-hazards and disaster events. This paper examines existing conceptual frameworks concerning exposure and risk assessments of natural catastrophe’s impacts. Furthermore, the paper identifies trends and gaps in risk assessment frameworks in the field of disaster studies that can be beneficial for maritime risk research. The authors propose a new risk assessment framework that can guide future research and multi-hazard risk assessment processes at different scales of maritime risks.     

The Dependency of People on the Great Barrier Reef,Australia

Understanding how people are dependent on Large Scale Marine Protected Areas (LSMPAs) is important for understanding how people might be sensitive to changes that affect these seascapes. We review how resource dependency is conceptualized and propose that it be broadened to include cultural values such as pride in resource status, scientific heritage, appreciation of aesthetics, biodiversity, and lifestyle opportunities. We provide an overview of how local residents (n = 3,181 face-to-face surveys), commercial fishers (n = 210, telephone surveys), and tourism operators (n = 119 telephone surveys) are potentially dependent on the Great Barrier Reef (GBR), a region currently experiencing significant environmental, social, and economic change. We found that commercial fishers and tourism operators were dependent not only financially on the GBR, but also because of their age, years in the industry and region, lack of education, and the number of dependents. These stakeholders lacked flexibility to secure alternative employment. All stakeholder groups, regardless of economic imperatives, were dependent on the GBR because of their cultural connections. We propose that resource dependency also provides an umbrella concept to describe the cultural services provided by an ecosystem, which can be described through place-based dependence and place-identity.     

Methodology for ro-ro ship and fleet sizing with application to short sea shipping

A novel methodology is developed for determining the characteristics of a cargo roll-on/roll-off (ro-ro) ship and the fleet size required for a given short sea shipping route. The ship and required fleet size to satisfy the transportation demand (for each pair of speed and freight rate) are determined using a database of existing cargo ro-ro ships to obtain the main technical characteristics of the most suitable ship. The time charter, voyage costs and revenue are then calculated considering the technical characteristics of each ship. Fuel costs are corrected for the actual ship speed and loading condition. A number of restrictions in the transportation problem are considered leading to the exclusion of unfeasible solutions. The maximum profit over the period of a year is identified among the feasible pairs of speed and freight rate. This general methodology is applied in a case study that considers the route between Leixões (Portugal) and Rotterdam (Netherlands). The study allows the identification of the most suitable ship and fleet sizes for different market penetration levels and quantifies the impact on shipping company profit of changes in parameters such as fuel costs, time charter costs, emission control area, installed propulsion power and stacking factor.     

A comparison of modelled and real-life driving profiles for the simulation of railway vehicle operation

A key factor in determining the performance of a railway system is the speed profile of the trains within the network. There can be significant variation in this speed profile for identical trains on identical routes, depending on how the train is driven. A better understanding and control of speed profiles can therefore offer significant potential for improvements in the performance of railway systems. This paper develops a model to allow the variability of real-life driving profiles of railway vehicles to be quantitatively described and predicted, in order to better account for the effects on the speed profile of the train and hence the performance of the railway network as a whole. The model is validated against data from the Tyne and Wear Metro, and replicates the measured data to a good degree of accuracy.     

Innovative strategies for urban car-sharing systems and a simulator to assess their performance

In this paper, three innovative car-sharing systems for urban areas are proposed, based on fleets of individual intelligent vehicles with three service characteristics: instant access, open-ended reservations and one-way trips. These features provide high flexibility but create an uneven distribution of vehicles among stations. Therefore, relocation of vehicles must be performed. Three different system procedures are proposed: in the first system, relocations are performed by users; in the other two, vehicles relocate automatically, thanks to their automation. In the first two systems, vehicles are accessible only at stations, whereas in the third they are also accessible along roads. In order to provide transport managers with a tool to test systems in different realities, an object-oriented simulator is developed. The simulation provides outputs of system performance, in terms of user waiting times and system efficiency. The proposed systems are simulated for the city of Genoa, in Italy, and a comparative analysis is presented.     

Analysis of factors that may be essential in the decision to fly on fully autonomous passenger airliners

This research investigates factors that influence opinion in the decision to fly on fully autonomous passenger airliners primarily from the perspective of aviation and technology professionals. Bayesian statistical inference and a two‐level fractional factorial survey are used to sample passengers' views on fully autonomous airliners. Eight trust, safety, and cost factors are incorporated into a vignette set in the future. Factors include automation levels, safety records, liability guarantees, airline integrity, and service disruptions. Dependent variables exist in five post‐vignette questions and essentially ask “Would you” or “Would you not” be willing to fly on a fully autonomous airliner? Sixteen versions of the vignette, each with unique trust, safety, and cost levels, present varying (unknown) degrees of influence to the survey respondents. For every demographic, the research shows a 99% statistically significant difference between the “prior” and “posterior” sampled population proportions willing to fly. The most significant positive influence involves integrity characteristics of the airline, while the most negative influence relates to life insurance liability guarantees. Research from 2003 suggested that this mode of travel would be acceptable to only 10.5% of respondents. When the 2003 research is used as a Bayesian prior probability, the resulting posterior probability for the demographics sampled can be modeled as a beta distribution, indicating 95% probability that the sampled proportion of the population willing to fly is between 33.2% and 36.4%. After adjusting for age and profession demographics to match the US population, the 95% probability bounds on the proportion willing to fly are 31.35% and 34.15%. Copyright © 2015 John Wiley & Sons, Ltd.     

Understanding members' carsharing (activity) persistency by using econometric model

Carsharing is an innovative travel alternative that has recently experienced considerable growth and become part of sustainable transportation initiatives. Although carsharing is becoming increasingly a popular alternative transportation mode in North America, it is still an under‐researched area. Current research is aimed at better understanding of the behavior of carsharing users. For every member, a two‐stage approach microsimulates the probability of being active in any month using a binary probit model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility‐based model. The model is estimated using empirical data from one of the largest carsharing companies in North America. The model estimates reveal that the activity persistency of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. It also shows that some attributes of the traveler (gender, age, and language spoken at home) impact his or her behaviors. Copyright © 2010 John Wiley & Sons, Ltd.