An analysis of the methods used to calculate the emissions of rolling stock in the UK

The aim of this paper is to analyse and compare the methods used for calculating emissions of UK rolling stock based on their type and mode of operation. The three modes under comparison were; diesel, electric and bi-mode. As well as comparing these three modes of operation, a comparison between Conventional, Freight and High Speed Rail was made. Alternate fuels were considered for diesel and bi-mode locomotives and compared based on their environmental impact. The emissions of trains were studied using three methods. Specifically, the three chosen methods were used to calculate the emissions of each train and a comparison of these methods was made. In the current UK energy climate, diesel trains emit less emissions than electric trains when factoring in mechanical and air resistances. Bi-mode trains have their place in the UK network but with electrification of the network currently in place, this mode of operation will become redundant in the near future. High Speed Rail, although time efficient, releases high emissions due to energy consumption increasing with the square of speed. Alternative fuels, such as biodiesel, should be a consideration for the future of rail, as emissions fall dramatically with content of biodiesel in fuel blends.     

Determining intermediate origin-destination matrices for the analysis of composite mode trips

We consider in this paper the problem of determining intermediate origin-destination matrices for composite mode trips that involve a trip by private car to a parking facility and the continuation of the trip to the destination either by walking or by a transit mode. The intermediate origin-destination matrices relate to each component of the composite mode trip: a matrix from the trip origins to intermediate destinations which are parking lots and a matrix from the parking lots to the final destinations. The approach that we propose to solve this problem is to modify the entropy based trip distribution models to consider inequality constraints related to parking lot capacities. Such models may be easily calibrated by using well known calibration methods or generalization of these methods and may be easily solved by applying a primal feasible direction method of nonlinear programming.     

A methodological framework for a priori selection of travel demand management package using fuzzy MCDM methods

Transportation - A robust and scientific selection of appropriate Travel Demand Management (TDM) measures is likely to ensure that the purpose of their implementation is met. The existing methods...     

Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data

The travel behavior of passengers from the transportation hub within the city area is critical for travel demand analysis, security monitoring, and supporting traffic facilities designing. However, the traditional methods used to study the travel behavior of the passengers inside the city are time and labor consuming. The records of the cellular communication provide a potential huge data source for this study to follow the movement of passengers. This study focuses on the passengers’ travel behavior of the Hongqiao transportation hub in Shanghai, China, utilizing the mobile phone data. First, a systematic and novel method is presented to extract the trip information from the mobile phone data. Several key travel characteristics of passengers, including passengers traveling inside the city and between cities, are analyzed and compared. The results show that the proposed method is effective to obtain the travel trajectories of mobile phone users. Besides, the travel behavior of incity passengers and external passengers are quite different. Then, the correlation analysis of the passengers’ travel trajectories is provided to research the availability of the comprehensive area. Moreover, the results of the correlation analysis further indicate that the comprehensive area of the Hongqiao hub plays a relatively important role in passengers’ daily travel.

     

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.     

Multilevel analysis of daily time use and time allocation to activity types accounting for complex covariance structures using correlated random effects

In this paper multilevel analysis is used to study individual choices of time allocation to maintenance, subsistence, leisure, and travel time exploiting the nested data hierarchy of households, persons, and occasions of measurement. The multilevel models in this paper examine the joint and multivariate correlation structure of four dependent variables in a cross-sectional and longitudinal way. In this way, observed and unobserved heterogeneity are estimated using random effects at the household, person, and temporal levels. In addition, random coefficients associated with explanatory variables are also estimated and correlated with these random effects. Using the wide spectrum of options offered by multilevel models to account for individual and group heterogeneity, complex interdependencies among individuals within their households, within themselves over time, and within themselves but across different indicators of behavior, are analyzed. Findings in this analysis include large variance contribution by each level considered, clear evidence of non-linear dynamic behavior in time-allocation, different trajectories of change in time allocation for each of the four dependent variables used, and lack of symmetry in change over time characterized by different trajectories in the longitudinal evolution of each dependent variable. In addition, the multivariate correlation structure among the four dependent variables is different at each of the three levels of analysis.