A note on consistency of choice units in entropy models

In using entropy maximization models to forecast locational and travel behaviour, one is confronted with the problem of delineating the choice process as precisely as possible. In addition to defining a fine-grain choice structure implying individuals seeking distinct location sites within residential zones and travelling to distinct jobs or shops within destination zones, this note also accounts for the fact that the location choice is of a site for a household or firm, but the corresponding travel choices are by individual members of a household. In conjunction with disaggregation across quantities with large variance, the above principles are applied to formulate improved versions of residential and shopping location models.     

Train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: Nonlinear integer programming models with linear constraints

This paper focuses on how to minimize the total passenger waiting time at stations by computing and adjusting train timetables for a rail corridor with given time-varying origin-to-destination passenger demand matrices. Given predetermined train skip-stop patterns, a unified quadratic integer programming model with linear constraints is developed to jointly synchronize effective passenger loading time windows and train arrival and departure times at each station. A set of quadratic and quasi-quadratic objective functions are proposed to precisely formulate the total waiting time under both minute-dependent demand and hour-dependent demand volumes from different origin–destination pairs. We construct mathematically rigorous and algorithmically tractable nonlinear mixed integer programming models for both real-time scheduling and medium-term planning applications. The proposed models are implemented using general purpose high-level optimization solvers, and the model effectiveness is further examined through numerical experiments of real-world rail train timetabling test cases.     

Maximum likelihood,entropy maximization,and the geometric programming approaches to the calibration of trip distribution models

Geometric programming is used to establish a formal primal-dual relationship between the maximum likelihood and the entropy maximization formulations of the trip distribution model. This relationship produces solution characteristics which agree with well known results. It provides an efficient procedure for interpreting and solving the model. A systematic method for conducting post-optimal sensitivity analysis is also available. The development is illustrated through a simple example.     

Incorporating time and income constraints in dynamic agent-based models of activity generation and time use: Approach and illustration

Existing theories and models in economics and transportation treat households’ decisions regarding allocation of time and income to activities as a resource-allocation optimization problem. This stands in contrast with the dynamic nature of day-by-day activity-travel choices. Therefore, in the present paper we propose a different approach to model activity generation and allocation decisions of individuals and households that acknowledges the dynamic nature of the behavior. A dynamic representation of time and money allocation decisions is necessary to properly understand the impact of new technologies on day to day variations in travel and activity patterns and on performance of transportation systems. We propose an agent-based model where agents, rather than acting on the basis of a resource allocation solution for a given time period, make resource allocation decisions on a day-by-day basis taking into account day-varying conditions and at the same time respecting available budgets over a longer time horizon. Agents that share a household interact and allocate household tasks and budgets among each other. We introduce the agent-based model and formally discuss the properties of the model. The approach is illustrated on the basis of simulation of behavior in time and space.     

The transport implications of activity and time budget constraints

Time budget and activity analysis ideas from sociological and geographical disciplines are reviewed in a transport context. A systematic examination of international data sources shows a regular structure, which applies only at a fairly aggregated level. The modelling structures so far used include stochastic models and entropy maximisation approaches: both appear to be able to replicate certain aspects of the linkage between and the duration spent in different activities, although from contradictory bases. The competition for travel time is shown as a function of stafe in family life cycle, with the pressure on trip combination, destination substitution, and on other activities such as sleep and leisure. The different forms of time expenditures at different locations are related to different appraisal purposes, including environmental exposure. Data from over 14 countries including Australia show broadly similar trends in travel time budgets, but little advance has yet been made in proving an effective model of the elasticity and cross-elasticity of demands for time for different activities. Moreover, the variations in personal budgets which show up at almost any level of disaggregation suggest that analysis by market segments will be essential. The first order variations in travel time budgets, income, and vehicle licence holding, and detailed analyses of household interview data should first be carried out to establish the links between time and financial budgets in satisfying the activity programs which transport sustains. The implications of an activity program approach for the valuation of time savings are considered.     

Routing and scheduling of RoRo ships with stowage constraints

Roll-on/Roll-off ships are used for international transport of vehicles and other rolling equipment. We consider the problem where a ship sails between two geographical regions, picking up cargo in the first and making deliveries to the second. Several variations are considered with optional cargoes, flexible cargo quantities, and ship stability restrictions. Decisions must be made regarding the route and schedule of the ship as well as the stowage of cargo onboard. The problem is modeled as a mixed integer program, which has been solved using Xpress. In addition, a tailor made heuristic procedure is built using components from tabu search and squeaky wheel optimization. Extensive computational results are presented, showing that the heuristic is able to handle realistically sized problem instances.     

The analysis of travel and emission impacts of travel demand management strategies using activity-based models

This paper demonstrates, tests and shows the value of activity-based travel demand models and household sample enumeration forecasting techniques in evaluating the transportation and air quality impacts of travel demand management strategies. Using data from the Portland, Oregon metropolitan area, three transportation policies were evaluated both individually and in combination: transit improvements, pricing, and telecommunications. The activity-based models used in this testing represents a significant improvement to today's "four-step" sequential model systems by providing a deeper insight into the individual decision making process in response to transportation policies. A wider range of impacts is predicted, and indirect effects as well as synergistic effects of such policies are taken into consideration. These models are capable of providing the information needed to improve the linkage of transportation models with emissions and air quality analysis methodologies by improving the prediction of variables that are important to accurately estimating emissions and air quality impacts of transportation actions.     

Multiple-phase train trajectory optimization with signalling and operational constraints

The train trajectory optimization problem aims at finding the optimal speed profiles and control regimes for a safe, punctual, comfortable, and energy-efficient train operation. This paper studies the train trajectory optimization problem with consideration of general operational constraints as well as signalling constraints. Operational constraints refer to time and speed restrictions from the actual timetable, while signalling constraints refer to the influences of signal aspects and automatic train protection on train operation. A railway timetable provides each train with a train path envelope, which consists of a set of positions on the route with a specified target time and speed point or window. The train trajectory optimization problem is formulated as a multiple-phase optimal control model and solved by a pseudospectral method. This model is able to capture varying gradients and speed limits, as well as time and speed constraints from the train path envelope. Train trajectory calculation methods under delay and no-delay situations are discussed. When the train follows the planned timetable, the train trajectory calculation aims at minimizing energy consumption, whereas in the case of delays the train trajectory is re-calculated to track the possibly adjusted timetable with the aim of minimizing delays as well as energy consumption. Moreover, the train operation could be affected by yellow or red signals, which is taken into account in the train speed regulation. For this purpose, two optimization policies are developed with either limited or full information of the train ahead. A local signal response policy ensures that the train makes correct and quick responses to different signalling aspects, while a global green wave policy aims at avoiding yellow signals and thus proceed with all green signals. The method is applied in a case study of two successive trains running on a corridor with various delays showing the benefit of accurate predictive information of the leading train on energy consumption and train delay of the following train.     

Private road competition and equilibrium with traffic equilibrium constraints

Toll road competition is one of the important issues under a build-operate-transfer (BOT) scheme, which is being encountered nowadays in many cities. When there are two or more competing firms and each firm operates a competitive toll road, their profits are interrelated due to the competitors' choices and demand inter-dependence in the network. In this paper we develop game-theoretic approaches to the study of the road network, on which multiple toll roads are operated by competitive private firms. The strategic interactions and market equilibria among the private firms are analyzed both in determining their supply (road capacity) and price (toll level) over the network. The toll road competition problems in general traffic equilibrium networks are formulated as an equilibrium program with equilibrium constraints or bi-level variational inequalities. Heuristic solution methods are proposed and their convergences are demonstrated with simple network examples. It is shown that private pricing and competition can be both profitable and welfare-improving.     

Integrating capacity analysis with high-speed railway timetabling: A minimum cycle time calculation model with flexible overtaking constraints and intelligent enumeration

Compared with most optimization methods for capacity evaluation, integrating capacity analysis with timetabling can reveal the types of train line plans and operating rules that have a positive influence on improving capacity utilization as well as yielding more accurate analyses. For most capacity analyses and cyclic timetabling methods, the cycle time is a constant (e.g., one or two hours). In this paper, we propose a minimum cycle time calculation (MCTC) model based on the periodic event scheduling problem (PESP) for a given train line plan, which is promising for macroscopic train timetabling and capacity analysis. In accordance with train operating rules, a non-collision constraint and a series of flexible overtaking constraints (FOCs) are constructed based on variations of the original binary variables in the PESP. Because of the complexity of the PESP, an iterative approximation (IA) method for integration with the CPLEX solver is proposed. Finally, two hypothetical cases are considered to analyze railway capacity, and several influencing factors are studied, including train regularity, train speed, line plan specifications (train stops), overtaking and train heterogeneity. The MCTC model and IA method are used to test a real-world case involving the timetable of the Beijing–Shanghai high-speed railway in China.     

The suitability of hedonic models for cost-benefit analysis: Evidence from commuting flows

We compare two estimates of benefits arising from the construction of new bridges in south-west Norway. One estimate comes from a hedonic property value model. Rather than follow an approach which is strictly theoretically correct, we adopt Rosen’s simple first-stage approach. To investigate and validate whether this simplified approach gives a reasonable estimate, we compare it to an estimate derived from a travel demand model. We find that a variant of an ex post hedonic house price model gives very similar estimates to the estimates from the travel demand model. This supports a hypothesis that the simplistic hedonic approach is reasonable.     

Optimal routing of a single vehicle with loading and unloading constraints

A real‐life situation in which a trucker has to collect a cargo of similar size from n different customers spread out in a given region and to deliver them to n locations spread out in another far‐away region has been formulated as a route‐design problem for a single vehicle. The minimal total time of loading, shipping and unloading is considered for different reshuffling methods, and the optimal method is determined. A solution procedure by enumeration is suggested to solve an actual small size problem, and an illustration is provided.     

Road user charging design: dealing with multi-objectives and constraints

This paper proposes an innovative approach for designing a road user charging scheme to meet multiple policy objectives. Three practical features are integrated into the design methodology including (i) cordon formation, (ii) a set of design constraints, and (iii) multiple objectives of the scheme. The methods also consider possible responses of road travellers to the charging scheme. Two methods based on genetic algorithms (GA) are developed for optimising a charging cordon scheme with constraints and with multiple objectives. The dynamic self-adaptive penalty GA and Non-dominated Sorting GA II (NSGA-II) are applied to the constrained design and multi-objective design respectively. The objective functions or constraints considered include social welfare improvement, revenue generation, and distributional equity impact. A case study of the City of Edinburgh is presented and common characteristics of charging cordon designs which perform well against the three objectives are discussed.

A probability analysis of some spatial interaction models

This paper derives several well-known spatial models in a framework based upon the laws of conditional probability analysis. In particular, it relates the structure of some existing models of trip distribution, elementary residential location and residential location with capacity constraints, to either the multinomial or hypergeometric probability distributions. The major changes from traditional methods for developing these models deal with the derivation and form of the objective function for each interaction model. This alternative analysis reaches a wider audience than that only familiar with entropy methods and leads to several improvements in generality. Further, when population constraints were imposed on residential location models, it was found that the model which developed naturally from the approach taken in the paper contained as a special case the model proposed by Dacey and Norcliffe and not the Wilson model.     

Home-based trip end models — A comparison between category analysis and regression analysis procedures

The trip end models which have been used in past transportation studies are briefly summarised. Problems associated with the use of zone-based models are outlined and reasons are given to support the development of models at the household rather than zonal level.It is suggested that recent developments which have taken place in household-based models have not been entirely logical. In particular, arguments between regression models and category analysis models have been confused with the use of aggregate (zonal) as against disaggregate (household) data — regression models being associated with the use of zonal data and category analysis models with household data. Misunderstood arguments and false notions regarding sample sizes have directed attention from the regression analysis approach.A detailed comparison of the category analysis and regression analysis methods for developing household-based trip end models is given. Both methods have been applied using data from the Monmouthshire Land Use Transportation Study. The regression results reported are from a very preliminary analysis and contain a number of anomalies, although it is thought that sufficient work has been done to provide an objective evaluation of the two methods.It is recommended that the household regression approach should be further investigated since it has advantages as a modelbuilding procedure and makes better use of sample data. A certain amount of categorisation of household types is necessary and the investigations would attempt to determine the best balance between categorisation and regression fitting. Further development will be restricted if the trend towards minimum sample sizes of about 1000 households is continued. Larger samples should be taken in certain circumstances to pursue development work.     

Investigation and analysis of evidence of asymmetric churn in travel demand models

There is a large amount of research work that has been devoted to the understanding of travel behaviour and for the prediction of travel demand and its management. Different types of data including stated preference and revealed preference, as well as different modelling approaches have been used to predict this. Essential to most travel demand forecasting models are the concepts of utility maximisation and equilibrium, although there have been alternative approaches for modelling travel behaviour. In this paper, the concept of asymmetric churn is discussed. That is travel behaviour should be considered as a two way process which changes over time. For example over time some travellers change their mode of travel from car to bus, but more travellers change their mode from bus to car. These changes are not equal and result in a net change in aggregate travel behaviour. Transport planners often aim at producing this effect in the opposite direction. It is important therefore to recognise the existence of churns in travel behaviour and to attempt to develop appropriate policies to target different groups of travellers with the relevant transport policies in order to improve the transport system. A data set collected from a recent large survey, which was carried out in Edinburgh is investigated to analyse the variations in departure time choice behaviour. The paper reports on the results of the investigation.     

Structural models for the analysis of traveler attitude-behavior relationships

Traveler attitudes and behavior have been shown to correlate in numerous previous studies. However, the correlation by itself leaves open the nature of the interrelationships between traveler attitudes and behavior. For example, attitudes could either cause or be caused by behavior. In fact, both options are concurrently possible. Structural equations are applied to a set of data gathered from Los Angeles central business district workers to ascertain the direction and nature of interrelationships between attitudes and behavior with respect to frequency of taking the bus to work. A mutual dependence between attitudes and behavior is demonstrated in the context of this dataset and behavioral choice situation; behavior and attitudes concurrently cause each other. In addition, it is found that two attitudinal components, perceptions of and affect toward a mode, function differently with respect to travel behavior.     

Discrete/continuous econometric models and their application to transport analysis

A wide range of transport‐related decisions involve the linking of discrete choices (e.g. of vehicle choice) and continuous choices (e.g. of vehicle use). In recent years econometricians have developed procedures for integrating such choices into a framework that is both economically and statistically sound. The literature is however somewhat technical. The objective of this paper is to provide a general overview of the basic elements of discrete/continuous econometric modelling with an emphasis on transport applications. It is hoped that such an introduction will demonstrate that the essence of the approach for the practitioner is quite straightforward and can be implemented with widely available computer software.     

The capitalization of subway access in home value: A repeat-rentals model with supply constraints in Beijing

Urban rail transit enhances accessibility of the communities it serves and often contributes to the value of local real properties. A small number of studies have gone beyond the traditional cross-sectional hedonic analysis to use repeat-sales data in the US for more robust estimation of rail transit’s impact on property value. While the empirical literature generally supports the positive capitalization of rail transit in home value, it has produced widely varied estimates and failed to incorporate the theoretical insight that more elastic housing supply would reduce the extent of local amenities’ capitalization in home value. Observing Beijing’s rapid expansion of subway lines during the late 2000s, this study uses repeat rental transactions from 2005 to 2011 to investigate how home value reacts to the change in the home’s distance to nearest subway station. Repeat-rentals estimates suggest a rent-distance elasticity of −0.02, 70% below the cross-sectional hedonic estimate. Moreover, using the unique history of state-owned enterprise relocations as instrument for intra-city variation in land supply, we find that the capitalization of subway proximity in home value is weaker where land supply is more elastic. Our findings support the significant bias caused by omitted variables in the hedonic estimates and confirm housing supply’s role in explaining the intra-city spatial variation in estimates.     

On the morning commute problem with bottleneck congestion and parking space constraints

Morning commuters choose their departure times based on a combination of factors—the chances of running into bottleneck congestion, the likely schedule delays, and parking space availability. This study investigates the morning commute problem with both bottleneck congestion and parking space constraints. In particular, it considers the situation when some commuters have reserved parking spots while others have to compete for public ones on a first-come-first-served basis. Unlike the traditional pure bottleneck model, the rush-hour dynamic traffic pattern with a binding parking capacity constraint varies with the relative proportions of the two classes of commuters. It is found that an appropriate combination of reserved and unreserved parking spots can temporally relieve traffic congestion at the bottleneck and hence reduce the total system cost, because commuters without a reserved parking spot are compelled to leave home earlier in order to secure a public parking spot. System performance is quantified in terms of the relative proportions of the two classes of commuters and is compared with those in the extreme cases when all auto commuters have to compete for parking and when none of them have to compete for one.