The aims of this thesis are to understand the issue of intermodal transport in Indonesia and to examine the impact of related policies on shifting to the rail mode; as an attempt to cut GHG emissions from containerized exports from Java. Stated preference (SP) and Revealed preference (RP) data of exporters and forwarders was collected for this purpose. This study employed four inland mode attributes (cost, time, reliability and GHG emissions) and two port attributes (port cost and ship calls frequency) to examine the alternatives. The SP-only and combined SP-RP data are employed to estimate the model using Multinomial Logit, Nested Logit, Mixed Multinomial Logit, and Mixed Nested Logit. The estimation results indicate that increases in inland mode cost, inland mode time, inland mode GHG emissions, and port cost all have very substantial adverse effects on the alternative’s utility. Conversely, inland mode reliability and frequency of ship calls have positive influence on the utility. Five single policies and four combined policies have been simulated using the best model gained from the estimation. Two single policies of cutting fuel subsidies for road mode and giving incentives to rail freight would provide the most important encouragement to modal shift. Nevertheless, the biggest reduction in GHG emissions can be obtained through policies of cutting fuel subsidies for road mode and putting restrictions on times and routes permitted for the road transport operations. The primary contribution of this research rests on its analysis of the exporters’ and freight forwarders’ attitudes related to GHG emissions, and the possible effects of policies that may be implemented to reduce GHG emissions. The novelty of this research is in its development of a joint model of port and inland mode choice from the exporters’ and forwarders’ perspective.

Under urbanisation, transport infrastructures may be improved when urban population grows. Meanwhile, land use patterns may vary and this urban dynamics may drive variations in mode choice of commuters and spatial features of transport networks. Empirical studies have observed scaling laws between the amount of transport infrastructures and city sizes. This thesis is aiming to provide a modelling framework for the analytical investigation of network growth and present some empirical observations of the variation in spatial features of transport networks. First, a simple linear monocentric city model is formulated and the global performance of transport systems is derived. Two cases according to strategies of urban intensification and sprawl have been studied to examine the consequence of the scaling-law growth in transport infrastructures. Second, this thesis proposes a modelling framework. The framework includes two congestible modes, the scaling-law growth of transport infrastructures and housing allocation of residents so that phenomena under urban dynamics could be modelled. The experiments show that the proposed modelling framework could investigate the trade-off of investment on the highway and public transport system. Third, empirical observations of spatial features in transport networks are reported in this thesis. The thesis measures circuity of transport networks, because this indicator could examine how aggregate transport networks are and the efficiency of network structures. Then research methods that can deal with several data sources are developed. The empirical observation shows that there is an exponential decay between the circuity and travel time in public transport networks. Meanwhile, this thesis also presents that the average circuity in road networks is less than that in public transport networks for the same sample of trips, which to some extent show the difference of spatial features between road and public transport networks. Additionally, correlations between circuity, accessibility and mode share are analysed.

The consumers’ increasing desire to be connected at all times and the advancement of integrated functionality within the vehicle, increases the risk that drivers could be faced with information overload while driving. Given the importance of human interaction with technology within the vehicle, automobile manufacturers are introducing workload manager systems within the vehicles to help prevent driver overload. However the ability of the system to decide in a timely manner requires anticipation of changes in workload, depending on the capacity of the driver and matching it with the demand expected from the driving task such as the dynamic traffic environment. In relation to the need to understand the influence of traffic demand on driver workload, the work here comprises the systematic manipulation of traffic complexity and exploration of workload measures to highlight which are sensitive to primary task demand manipulated. A within-subjects design was used in the studies explored in this thesis to allow comparison between different manipulated traffic conditions. In the first simulator test, the ability of various objective and subjective workload measures to tap into drivers’ momentary workload was examined. Following the identification of a subjective measure that was sensitive to the influence of lane changes performed by neighbouring vehicle on drivers’ momentary workload, the characteristics of the lane change were explored in the subsequent studies involving single and dual-task conditions. Overall, these studies suggested suppression of non-urgent communications by a workload manager during safety-critical conditions involving critical cut-ins would be advantageous to both younger and older drivers. This thesis offers a novel and valuable contribution to the design of a workload estimator so as to ensure that the driving demand is always within drivers’ capacity to avoid driver overload. Results of these studies have also highlighted the utility of vehicle-based sensor data in improving workload manager functionality.

This thesis develops a modelling framework for learning route choice behaviour of travellers on an underground railway system, with a major emphasis on the use of smart-card data. The motivation for this topic comes from two respects. On the one hand, in a metropolis, particularly those furnished with massive underground services (e.g. London, Beijing and Paris), severe passenger-traffic congestion may often occur, especially during rush hours. In order to support the public transport managers in taking actions that are more effective in smoothening the passenger flows, there is bound to be a need for better understanding of the passengers’ routing behaviour when they are travelling on such public transport networks. On the other hand, a wealth of travel data is nowadays readily obtainable, largely owing to the widespread implementation of automatic fare collection systems (AFC) as well as popularity of smart cards on the public transport. Nevertheless, a core limitation of such data is that the actual route-choice decisions taken by the passengers might not be available, especially when their journeys involve alternative routes and/or within-station interchanges. Mostly, the AFC systems (e.g. the Oyster system in London) record only data of passengers’ entry and exit, rather than their route choices. We are thus interested in whether it is possible to analytically infer the route-choice information based on the ‘incomplete’ data. Within the scope of this thesis, passengers’ single journeys are investigated on a station basis, where sufficiently large samples of the smart-card users’ travel records can be gained. With their journey time data being modelled by simple finite mixture distributions, Bayesian inference is applied to estimate posterior probabilities for each route that a given passenger might have chosen from all possible alternatives. We learn the route-choice probabilities of every individual passenger in any given sample, conditional on an observation of the passenger’s journey time. Further to this, the estimated posterior probabilities are also updated for each passenger, by taking into account additional information including their entry times as well as the timetables. To understand passengers’ actual route choice behaviour, we then make use of adapted discrete choice model, replacing the conventional dependent variable of actual route choices by the posterior choice probabilities for different possible outcomes. This proposed methodology is illustrated with seven case studies based in the area of central zone of the London Underground network, by using the Oyster smart-card data. Two standard mixture models, i.e. the probability distributions of Gaussian and log-normal mixtures, are tested, respectively. The outcome demonstrates a good performance of the mixture models. Moreover, relying on the updated choice probabilities in the estimation of a multinomial logit latent choice model, we show that we could estimate meaningful relative sensitivities to the travel times of different journey segments. This approach thus allows us to gain an insight into passengers’ route choice preferences even in the absence of observations of their actual chosen routes.

Reliability is considered the most important attribute of transit service by passengers. There are congested transit environments wherein even if a transit service is perfectly on schedule it is termed unreliable from a passenger’s perspective when they are unable to board the first service of their choice set. The phenomenon ‘failure to board’ arises in congested transit networks having strict physical capacity constraints wherein the transit service cannot take in passengers beyond its capacity. This results in some of the passengers being left waiting for the next service at the transit stops. The existing transit assignment models; be it hyperpath based effective frequency models, Bureau of Public Roads (BPR) based route section models or aggregate stochastic process models with strict capacity constraints, all assume that the passengers have perfect knowledge of the network seldom discussing the sources of such information. In the current thesis this assumption is renounced and a reliability based disaggregate stochastic process model with strict capacity constraints (R-DSPM) using route section approach is proposed such that each passenger in the absence of information updates his/her route choice based on their individual experience. Though the aggregate stochastic process model implements the strict capacity constraint for each transit service generated; the model along with the assumption of perfect knowledge of the network assumes that the passengers are risk neutral. The proposed R-DSPM implements a strict capacity constraint for each transit service generated thereby accounting for failure to board situation in congested network. The proposed model differs from the existing aggregate stochastic process model in its assumption of risk averse passengers. Risk aversion in R-DSPM considers variance associated with:- interarrival times of transit service at the transit stop; the waiting time of passengers due to the ‘failure to board’ condition; the in-vehicle travel times of routes comprising of route sections containing more than one attractive line section and the variable demand generated for each day’s travel. The risk aversion of each passenger is accounted for in R-DSPM through the linear combination of mean total travel time and total travel variance (mean-variance) and a linear combination of mean total travel time and expected lateness (mean-lateness). A generic day to day framework is developed with markovian properties such that it enables the integration of both mean-variance and mean-lateness costs with ease and results in a unique stationary distribution of costs and flows for each route. The proposed R-DSPM thus accounts for: strict capacity constraints of transit vehicle, passengers learning process, risk aversion of passengers, differences in passenger perceptions, day to day variability in demand and supply of transit network. The micro simulation framework shows through implementation on example networks that while accounting for passenger’s risk aversion the R-DSPM is able to arrive at a unique stationary distribution irrespective of its initial conditions. The sensitivity of the proposed R-DSPM with strict capacity constraint under different parameter assumptions has been carried out . A calibration of the parameters involved in the route section based BPR styled cost function and the hyperpath based effective frequency cost function using the proposed R-DSPM indicates that different congestion function parameters are required for different sections of a transit network. It is also shown through implementation on an example network that the proposed R-DSPM framework enables the passengers to learn about the reliability of routes and strategies. At higher dispersion values R-DSPM assign risk averse passengers such that the standard deviation of flows and experienced total travel time on various routes and strategies are lesser than that obtained by accounting for risk aversion using the aggregate stochastic process models. The impact of accounting for risk aversion on various policy measures that could be carried out by the operators to improve the waiting time reliability of passengers is also assessed using the proposed R-DSPM with strict capacity constraints. It is shown that for certain parameter assumptions and for certain policy measures the assumption of risk aversion in transit assignment could result in an entirely different reliability profile from that of an assignment process assuming risk neutral passengers. The implementation of the proposed R-DSPM with strict capacity constraints on a real network has been carried out on a section of London underground and several possible policy measures have been evaluated. The evaluation of policies has further emphasised the need to consider the risk aversion in passengers especially to account for the number of passengers preferring to make a transfer (in absence of transfer penalty) at the transfer stops to minimise their risk aversion costs.

Travel behaviour change is traditionally regarded as being difficult to achieve, with strategies and initiatives often generating only slow and incremental shifts in behaviour amongst the population. There is an emerging discussion in the literature that more radical approaches to travel behaviour change are needed, to contribute to achieving challenging decarbonisation targets. If a step change is required then one potential source of learning is the study of disruptions to systems of mobility provision, which may provide valuable insights into how more radical travel behaviour change is achieved and, potentially, sustained. This thesis provides an innovative approach to examining major-event disruption, in this case arising from the London 2012 Olympic and Paralympic Games, to understand the potential for change from such large, disruptive events. A four-wave longitudinal panel survey was applied to establish the extent, and longevity, of change in response to the Games. The research uses the Transtheoretical Model (TTM) to critically examine travel behaviour. The results show that change was extensive during the Games (54% made at least one change); however change was not often sustained afterwards. Reducing, relocating and re-timing were the most common changes. The key elements of the TTM were not well suited to studying change in such a context, however less commonly used constructs of the model contributed to the identification of four clusters within the sample that provided valuable insight into the behaviour observed. This research makes a valuable contribution to the growing literature around the potential for learning, and opportunities for change, when there is an imperative to do so. Whilst the longevity of changes to travel were limited, the research provided greater understanding of the adaptability and planning involved in response to major-event disruption, and what this means for future travel planning. The clusters generated helped to show the psychological constructs important for supporting different types of change, which can contribute to approaching and understanding travel behaviour change in broader contexts, when there is an imperative to change.

The Indian Railways (IR) have, over the past four decades, been steadily losing market share, in both passenger and freight markets. In the case of freight, they have gone from being the dominant mode to being carriers of bulk traffic only. Most of the general goods, high value, traffic has shifted to road. In line with the pattern of economic growth, the manufactured goods sector is the fastest growing sector of the economy. This leads on one hand, to exclusion of JR from an important, and growing, sector of the economy and on the other hand to heavy strains on the already saturated road network, higher environmental dis-benefits and higher costs of petroleum imports. The Container Corporation of India (CONCOR), a subsidiary of IR, is now attempting to enter the domestic freight market, to recapture some of this freight traffic. The present work has been taken up, with the final objective of developing a methodology, for identifying sectors where viable intermodal services can be offered, in comparison to road, as well as rail, services and to determine the price and service levels required for the same. In the absence of any revealed preference (RP) data, as well as any previous work on valuation of attributes for the different sectors, we have used an Adaptive Stated Preference (SP) design for our work. The Leeds Adaptive Stated Preference (LASP) software has been modified and used for the work. Various alternatives have also been examined, with regard to the approach to be used for analysis of the survey data and we have finally decided to use individual level models aggregated using weighted averages as these appear to provide the most robust estimates. We have developed models for costing of, door to door, freight movement by road, rail and intermodal services. These models have been used in conjunction with the demand model to assess the viability of the different services for the sectors considered. Our findings indicate that, using fully allocated door to door costs, rail is a clear leader for distances over about 500 Km, on cost basis alone. However, when the service quality factors are taken into account, intermodal services become more attractive for the high value, damage prone, products while road services are more attractive for the lower value products. Rail services break even under 1500 Km only in a few of the situations considered by us and Intermodal service break even under 1500 Km for a large number of the situations (in case of use of new high speed wagons this breakeven shifts to between 500 to 1000 Km). Rail services would need to match the quality of road services, or be priced on marginal cost basis, to be competitive, as compared to road services. Intermodal services can be quite profitable, with presently attained transit times using the older (BFK) wagons, if they are offered at least thrice a week. The larger firms also appear to be more likely to go for intermodal services, than smaller firms. In case of the newer, high speed wagons, the increased capital costs are offset by the gains due to faster turn-around and there is a substantial improvement in the quality of service (time & reliability) that can be provided. This provides an opportunity for a highly profitable service to be provided with the induction of the new wagons.

Within a highway transportation network, the social welfare implications of two different groups of agents setting tolls in competition for revenues are studied. The first group comprises private sector toll road operators aiming to maximise revenues. The second group comprises local governments or jurisdictions who may engage in tax exporting. Extending insights from the public economics literature, jurisdictions tax export because when setting tolls to maximise welfare for their electorate, they simultaneously benefit from revenues from extra-jurisdictional users. Hence the tolls levied by both groups will be higher than those intended solely to internalise congestion, which then results in welfare losses. Therefore the overarching question investigated is the extent of welfare losses stemming from such competition for toll revenues. While these groups of agents are separately studied, the interactions between agents in each group in competition can be modelled within the common framework of Equilibrium Problems with Equilibrium Constraints. Several solution algorithms, adapting methodologies from microeconomics as well as evolutionary computation, are proposed to identify Nash Equilibrium toll levels. These are demonstrated on realistic transportation networks. As an alternative paradigm to competition, the possibilities for co-operation between agents in each group are also explored. In the case of toll road operators, the welfare consequences of competition could be positive or adverse depending on the interrelationships between the toll roads in competition. The results therefore generalise those previously obtained to a more realistic setting investigated here. In the case of competition between jurisdictions, it is shown that the fiscal externality of tax exporting resulting from their toll setting decisions can substantially reduce the welfare gains from internalising congestion. The ability of regulation, co-operation and bilateral bargaining to reduce the welfare losses are assessed. The research thus contributes to informing debates regarding the appropriate level of institutional governance for toll pricing policies.

This thesis is concerned with the development of a composite resilience index for road transport networks. The index employs three characteristics, namely redundancy, vulnerability and mobility, measuring resilience at network junction, link and origin-destination levels, respectively. Various techniques have been adopted to quantify each characteristic and the composite resilience index as summarised below. The redundancy indicator for road transport network junctions is based on the entropy concept, due to its ability to measure the system configuration in addition to being able to model the inherent uncertainty in road transport network conditions. Various system parameters based on different combinations of link flow, relative link spare capacity and relative link speed were examined. The developed redundancy indicator covers the static aspect of redundancy, i.e. alternative paths, and the dynamic feature of redundancy reflected by the availability of spare capacity under different network loading and service level. The vulnerability indicator for road transport network links is developed by combining vulnerability attributes (e.g. link capacity, flow, length, free flow and traffic congestion density) with different weights using a new methodology based on fuzzy logic and exhaustive search optimisation techniques. Furthermore, the network vulnerability indicators are calculated using two different aggregations: an aggregated vulnerability indicator based on physical characteristics and the other based on operational characteristics. The mobility indicator for road transport networks is formulated from two mobility attributes reflecting the physical connectivity and level of service. The combination of the two mobility attributes into a single mobility indicator is achieved by a fuzzy logic approach. Finally, the interdependence of the proposed characteristics is explored and the composite resilience index is estimated from the aggregation of the three characteristics indicators using two different approaches, namely equal weighting and principal component analysis methods. Moreover, the impact of real-time travel information on the proposed resilience characteristics and the composite resilience index has been investigated. The application of the proposed methodology on a synthetic road transport network of Delft city (Netherlands) and other real life case studies shows that the developed indicators for the three characteristics and the composite resilience index responded well to traffic load change and supply variations. The developed composite resilience index will be of use in various ways; first, helping decision makers in understanding the dynamic nature of resilience under different disruptive events, highlighting weaknesses in the network and future planning to mitigate the impact of disruptive events. Furthermore, each developed indicator for the three characteristics considered can be used as a tool to assess the effectiveness of different management policies or technologies to improve the overall network performance or the daily operation of road transport networks.

The value of travel time is a key input for the evaluation and comparison of transport projects. Travel time savings often constitute a great part of the benefits of a project, and therefore the value assigned to them is crucial for cost-benefit analyses. Less often, there may be some transport projects which lead to travel time increases (e.g. repair or maintenance works). In general, it is essential to determine the valuation of travel time changes (VTTC). The VTTC is defined as the marginal rate of substitution between travel time and travel cost for an individual. Hence, the VTTC is likely to differ across people and even for the same person under different circumstances. Therefore, for policy-making it is recommendable to have a set of VTTC based on factors that could influence time valuation: e.g. income or journey length. Stated choice (SC) experiments constitute the most popular method to estimate the set of VTTC of a population. These experiments offer travellers hypothetical choice scenarios to observe their VTTC. Therefore, the choice context may also play on role on valuation: e.g. size and sign of the time changes offered. The choices are analysed using discrete choice models to estimate the VTTC. The aim of this thesis is to increase our understanding of a population’s underlying set of VTTC. To achieve this target, we first explore a series of key sources of variation of the VTTC and relate them within the framework of microeconomic theory. Potential confounding between sources is investigated. Secondly, this thesis identifies, relates and compares two popular modelling approaches to estimate the VTTC: Random Utility and Random Valuation. Finally, our research analyses the role of the design variables used in the SC experiment on the estimation of the set of VTTC. The empirical work has been carried out using datasets from the last national VTTC studies in the UK and Denmark. The results provide valuable insights, from which would highlight the following: i) the Random Valuation approach proves to be superior to the traditional Random Utility approach, and in general gives a systematically lower VTTC, ii) confounding is apparent between some sources of variation and the design variables influence most model estimates that determine the set of VTTC, iii) journey length effects do not exist in the data explored, as opposed to what earlier works report. The findings of this thesis have important implications for appraisal.