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This paper explores route‐choice patterns among motorists on a scenic road in south‐western Norway. Factor analysis is used to identify the characteristics of the types of motorists who seek particular benefits when they choose to travel along this specific route. Five factors were found: 1) experiencing landscapes and attractions; 2) outdoor recreation; 3) off the beaten track; 4) a suitable road; and 5) a variation of travel experiences. In conclusion, the paper offers an alternative framework of road choice structures.

This study reports on the effect of demand variation on the optimal location of road-pricing cordons. The optimal road-pricing cordon, in this study, aims to maximise the social welfare function. This optimisation program is categorised as Bi-level optimisation programming which is a NP hard problem. The paper first describes the method developed to solve the optimal toll problem for a given set of chargeable links. The tests were carried out with a small toy network and a larger scale network. For the small network, four single user class demand characteristics were varied individually; these were the elasticity of trip generation with respect to increases in travel cost, value of travel time, volume of traffic, and traffic distribution pattern. For the larger scale network, only elasticity, value of time, and trip volume were tested. The results of the larger scale network are also analysed by including the cost per toll point. The tests with the larger scale network were modified so that the constraint of uniform charge is applied. The results showed that demand variation could influence the best location of toll points. This finding raises the question of whether the implementation of the same cordon all day in an urban traffic network is the optimal approach under the existence of demand variations by time of day, and also whether the evaluation process of the cordon location should consider the effect of different time periods together.

We review what is known and what is still unknown about the process of revealing the impact of unreliability on travel choices. We do this from the perspective of a demand-modelling practitioner who wishes to allow for the benefits from improved reliability in the assessment of a transport scheme. We discuss the travel responses affected by unreliability, the requirements from the data used to model these responses, the explanatory variables used in these models and the additional information required as input when applying them. One of our findings is that there is a conflict between existing studies in their conclusions about the aversion to early arrival. Another notion is that it is unclear whether the common simplified treatment of the distribution of preferred arrival times is acceptable. We also suggest that the dominance of departure time shifting as a primary response to unreliability might refute the common assumptions about travellers' choice hierarchy, which was established without considering the impact of unreliability; this raises questions about the robustness of assignment models that do not allow time shifting.

During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data.

First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method.

The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers.

First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.

Computer models are often used for studying the effects of changing conditions in the road network. State-of-the-art macroscopic models generally take some kind of network equilibrium approach and therefore have difficulties in appropriately representing short-term capacity reductions, probably resulting in too low estimates of delays. Recently developed dynamic models may be more promising. The purpose of this paper is to investigate the implications of model choice further, as well as the possibilities to study effects of short-term incidents. Three different computer programs were used: TRACKS, SATURN, and Paramics. The results show that microsimulation is a feasible tool for studying short-term disturbances in the road transportation system.

The paper aims to identify and analyze passengers’ riding paths for providing better operational support for digital transformation in megacity metro systems.

The authors develop a method to leverage certain passengers’ deterministic riding paths to corroborate other passengers’ uncertain paths. Using Automatic Fare Collection data and train schedules, a witness model is built to recover the actual riding paths for passengers whose paths are unknown otherwise. The identification and analysis of passenger riding paths between three different types of origin–destination) pairs reveal the complexity of passenger path choice.

The results show that passenger path choice modeling is usually characterized by complexity, experience and partial blindness. Some passengers choose paths that are not optimal due to their experience and limited access to overall metro system information. These passengers could be the subject of improved path guidance in light of riding efficiency improved through digital transformation.

This research contributes to the improvement of metro management and operations by leveraging ongoing digital transformation in megacity metro systems. Based on the riding paths and trip chains of a large number of individual passengers identified by the proposed method, metro operation management could prevent risks in areas with concentrated passenger flow in advance, optimally adjust train schedules on a daily basis and deliver real-time riding guidance station by station, which would greatly improve megacity metro systems’ service safety, quality and operational efficacy over time.

Computer‐aided decision‐support tools are part and parcel of the emergency planning and management process today. Much is dependent on using modern technology to gather and analyse data on damage assessment, meteorology, demography, etc. and provide decision support for prevention/mitigation, response and recovery. Diverse technologies are merged to provide useful functions to aid the emergency planner/manager. Complexities arise when attempting to link several streams of technology to achieve a realistic, usable and reliable decision‐support tool. This discussion identifies and analyses the challenging issues faced in linking two technologies: simulation modelling and GIS, to design spatial decision‐support systems for evacuation planing. Experiences in designing CEMPS, a prototype designed for area evacuation planning, are drawn on to discuss relevant managerial, behavioural, processual and technical issues. Focus is placed on modelling evacuee behaviour, generating realistic scenarios, validation, logistics, etc. while also investigating future trends and developments.

Independent travelers are those vacationers who have booked only a minimum of their transportation and accommodation arrangements prior to departure on the vacation. Independent travel is an important and growing sector of worldwide tourism. Choice of vacation itinerary for the independent vacation represents a complex series of decisions regarding purchase of multiple leisure and tourism services. This chapter builds and tests a model of independent traveler decision-making for choice of vacation itinerary. The research undertaken employs a two-phase, inductive–deductive case study design. In the deductive phase, the researcher interviewed 20 travel parties vacationing in New Zealand for the first time. The researcher interviewed respondents at both the beginning and the end of their New Zealand vacations. The study compares pre-vacation research and plans, and actual vacation behaviors, on a case-by-case basis. The study examines case study narratives and quantitative measures of crucial variables. The study tests two competing models of independent traveler decision-making, using a pattern-matching procedure. This embedded research design results in high multi-source, multi-method validity for the supported model. The model of the Independent Vacation as Evolving Itinerary suggests that much of the vacation itinerary experienced in independent travel is indeed unplanned, and that a desire to experience the unplanned is a key hedonic motive for independent travel. Rather than following a fixed itinerary, the itinerary of an independent vacation evolves as the vacation proceeds. The independent traveler takes advantage of serendipitous opportunities to experience a number of locations, attractions and activities that they had neither actively researched nor planned.