Measuring dissimilarity of geographically dispersed space�Ctime paths

In activity-travel analysis, sequences are analysed both in space and time. From this perspective, sequence alignment methods (SAM) are used to value the dissimilarity of sequences. However, only a limited number of research efforts account for spatial characteristics of activity-travel sequences. Additionally, the existing techniques considering spatial characteristics are mainly suited to compare sequences within a small study area. Therefore, the present research re-designs a multidimensional dissimilarity measure which enables identifying dissimilarities between sequences which are geographically dispersed. This technique includes transforming the geographical coordinates of activity locations to Angle/Arc Length (AAL)-trajectories to capture the relative geographical movements within each sequence. These AAL-trajectories form the basis of the subsequent multidimensional sequence alignment analysis aimed at estimating the dissimilarity between activity-travel sequences. This approach proves to compare activity-travel sequences based on the relative positions of the activity locations within sequences, rather than founded on the distances between the absolute geographical locations, as is the case in the traditional sequence alignment methods.     

A review of input–output models on multisectoral modelling of transportation–economic linkages

Understanding the role of transportation in urban and regional economy is a persistent analytical topic within the transportation research community. Multi-sectoral input–output (IO) modelling, as a standard economic analysis tool, has great advantages in reflecting industrial interdependencies in an economy. The simplicity of IO and the well-known concept of multiplier effect also make it broadly used in both academia and practice. This paper provides an introduction of IO models and reviews the past IO studies from 2000 onward on modelling transportation–economic linkages. The following types of models are included: single-region, multi-region and random utility-based multi-region IO models, with central methodological features described. An evaluation of modelling issues brought to light by reviewing the literature is then presented. For future research, more critical attention should be directed towards IO’s modelling assumptions, spatial linkages and the static representation of the economy. In addition, there are needs for more attention in the following areas: sectoral aggregation, specification of household sector and the integration with transportation forecasting models. The paper concludes with brief recommendations on future IO applications.     

Estimating the most likely space–time paths,dwell times and path uncertainties from vehicle trajectory data: A time geographic method

Global Positioning System and other location-based services record vehicles’ spatial locations at discrete time stamps. Considering these recorded locations in space with given specific time stamps, this paper proposes a novel time-dependent graph model to estimate their likely space–time paths and their uncertainties within a transportation network. The proposed model adopts theories in time geography and produces the feasible network–time paths, the expected link travel times and dwell times at possible intermediate stops. A dynamic programming algorithm implements the model for both offline and real-time applications. To estimate the uncertainty, this paper also develops a method based on the potential path area for all feasible network–time paths. This paper uses a set of real-world trajectory data to illustrate the proposed model, prove the accuracy of estimated results and demonstrate the computational efficiency of the estimation algorithm.     

A multi-commodity Lighthill–Whitham–Richards model of lane-changing traffic flow

Systematic lane changes can seriously deteriorate traffic safety and efficiency inside lane-drop, merge, and other bottleneck areas. In our previous studies (Jin, 2010a, Jin, 2010b), a phenomenological model of lane-changing traffic flow was proposed, calibrated, and analyzed based on a new concept of lane-changing intensity. In this study, we further consider weaving and non-weaving vehicles as two commodities and develop a multi-commodity, behavioral Lighthill–Whitham–Richards (LWR) model of lane-changing traffic flow. Based on a macroscopic model of lane-changing behaviors, we derive a fundamental diagram with parameters determined by car-following and lane-changing characteristics as well as road geometry and traffic composition. We further calibrate and validate fundamental diagrams corresponding to a triangular car-following fundamental diagram with NGSIM data. We introduce an entropy condition for the multi-commodity LWR model and solve the Riemann problem inside a homogeneous lane-changing area. From the Riemann solutions, we derive a flux function in terms of traffic demand and supply. Then we apply the model to study lane-changing traffic dynamics inside a lane-drop area and show that the smoothing effect of HOV lanes is consistent with observations in existing studies. The new theory of lane-changing traffic flow can be readily incorporated into Cell Transmission Model, and this study could lead to better strategies for mitigating bottleneck effects of lane-changing traffic flow.     

Estimation of population origin–interchange–destination flows on multimodal transit networks

Previous research has combined automated fare-collection (AFC) and automated vehicle-location (AVL) data to infer the times and locations of passenger origins, interchanges (transfers), and destinations on multimodal transit networks. The resultant origin–interchange–destination flows (and the origin–destination (OD) matrices that comprise those flows), however, represent only a sample of total ridership, as they contain only those journeys made using the AFC payment method that have been successfully recorded or inferred. This paper presents a method for scaling passenger-journey flows (i.e., linked-trip flows) using additional information from passenger counts at each station gate and bus farebox, thereby estimating the flows of non-AFC passengers and of AFC passengers whose journeys were not successfully inferred.The proposed method is applied to a hypothetical test network and to AFC and AVL data from London’s multimodal public transit network. Because London requires AFC transactions upon both entry and exit for rail trips, a rail-only OD matrix is extracted from the estimated multimodal linked-trip flows, and is compared to a rail OD matrix generated using the iterative proportional fitting method.     

A Gendered Perspective of the Shelter–Transport–Livelihood Link: The Case of Poor Women in Delhi

Abstract

Women form an important part of the workforce originating from the slums in the city of Delhi, India. The paper illustrates that women spend more time travelling on slower modes of transport to access work; the faster modes are more expensive. Their time–poverty demands they look for work at shorter distances from home. The basic argument presented is that their ability to contribute to the alleviation of their standard of living and their status in society is severely curtailed by their limited mobility and the constrained accessibility to the transport system of the city. This transport deprivation becomes further exacerbated by the process of forced eviction and relocation of low‐income households to the periphery of the city, causing the women to lose livelihood opportunities.     

Valuation of strategic options in public–private partnerships

This paper investigates the feasibility of and develops an economic valuation model for strategic options in Comprehensive Development Agreements (CDAs). A CDA is a form of public–private partnership whereby the right to price and collect revenues from toll roads is leased to a private entity for a long but finite period of time. In exchange, this provides local and state governments with a quick influx of cash and/or additional infrastructure. Uncertainty associated with such long-term leases is of substantial public concern. This paper examines five different strategic options, namely a buyout option, a conditional buyout option, a revenue-sharing option, and two types of minimum revenue guarantee options. The buyout option in particular could give the public sector additional control over the future use of leased facilities and address potential concerns regarding long-run uncertainty and possible unforeseen windfalls for the private sector. The paper’s contributions include the analysis, feasibility assessment and valuation of several strategic options, sensitivity analysis of the solutions, an economic consumer demand-based revenue model for purposes of cash flow simulation, and analysis of option price sensitivity to “moneyness”. The main conclusion is that strategic options can provide useful risk reduction, but generally have significant value relative to the lease itself. By scaling down payoffs, options could be realistically included in CDAs and other PPPs. For some parameter values, option values to the developer and public authority are offsetting, allowing for costless risk reduction.     

Applications of Random-Utility-based Multi-region Input–Output Models of Transport and the Spatial Economy

Abstract

An introduction to random-utility-based multiregional input–output models used for the purpose of spatial economic and transport interaction modelling is provided. The main methodological developments and important results of a dozen applications from the years 1996–2013 are described. This is followed by an outlook of potential future directions. Further research is mainly needed in five areas: (a) overall validation of the method, perhaps through back-casting applications on infrastructure plans with observed trade impacts; (b) extensions of trade coefficient models to add realism and improve accuracy; (c) the use of multi-scale modelling to capture interdependencies between geographical scales and to improve the representation of exports and imports; (d) improvements in the representation of price effects, as well as innovation and technological progress, by way of variable technical coefficients; and (e) a deeper investigation of the algorithm used to include elastic selling prices.     

Competitiveness of container terminal operating companies in South Korea and the industry–university–government network

Burgeoning container port facilities have fostered intensified competition among container terminal operating companies (CTOCs). However, despite research into their survival strategies which identified antecedents of competitiveness including hard factors such as facilities, available cargo and cargo processing ability, softer factors spanning human resource management, networks and strategic alliances with universities and government agencies in industry–university–government (I–U–G) networks have been overlooked. This study aims to examine both hard and softer antecedents of competitiveness as perceived by 152 professionals in South Korean CTOCs; empirical relationships among these antecedents, I–U–G networks, and competitiveness itself; and the significance of the I–U–G network in establishing and improving competitiveness. Posited antecedents of competitiveness included human resources, facilities, service quality, customer orientation, reputation, and government support policy as independent variables; the I–U–G network as a moderating variable; and competitiveness as a dependent variable. Empirical structural relationships revealed that excepting government support policy, each variable significantly affected CTOC competitiveness. Further, the I–U–G network moderated the relationships between the antecedents of competitiveness and competitiveness. Because an effective I–U–G network was pivotal in controlling CTOC competitiveness, improved competitiveness requires not only differentiation of human resources, facilities, service quality, customer orientation, and reputation factors but also I–U–G network developments.     

A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

Development of origin–destination matrices using mobile phone call data

In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.     

Activity–travel behaviour of non-workers from Bangalore City in India

This paper presents exploratory and statistical analyses of the activity–travel behaviour of non-workers in Bangalore city in India. The study summarises the socio-demographic characteristics as well as the activity–travel behaviour of non-workers using a primary activity–travel survey data collected by the authors. Where possible, the research also compares the analysis findings with the case studies on activity–travel behaviour of non-workers, carried out in developed and developing countries. This gives an opportunity to understand the differences/similarities in the activity–travel behaviour of non-workers across diverse socio-cultural settings. The preliminary exploratory analysis shed light on the differences in activity participation, trip chaining, time-of-day preference for trip departure, and mode use behaviour of non-workers in Bangalore city. Statistical models were developed for investigating the effects of individual and household socio-demographics, land use parameters, and travel context attributes on activity participation, trip chaining, time-of-day choice, and mode choice decisions of non-workers. A few important results of the analysis are the influence of viewing television at home on out-of-home activity participation and trip-chaining behaviour, and the impact of in-home maintenance activity duration on time-of-day choice. Further, based on the findings of the initial analyses, an attempt has been made in this study to develop an integrated model that links time allocation, time-of-day choice, and trip chaining behaviour of non-workers. The study also discusses the implications of the research findings for transportation planning and policy for Bangalore city.     

Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts

This paper proposes a generalized model to estimate the peak hour origin–destination (OD) traffic demand variation from day-to-day hourly traffic counts throughout the whole year. Different from the conventional OD estimation methods, the proposed modeling approach aims to estimate not only the mean but also the variation (in terms of covariance matrix) of the OD demands during the same peak hour periods due to day-to-day fluctuation over the whole year. For this purpose, this paper fully considers the first- and second-order statistical properties of the day-to-day hourly traffic count data so as to capture the stochastic characteristics of the OD demands. The proposed model is formulated as a bi-level optimization problem. In the upper-level problem, a weighted least squares method is used to estimate the mean and covariance matrix of the OD demands. In the lower-level problem, a reliability-based traffic assignment model is adopted to take account of travelers’ risk-taking path choice behaviors under OD demand variation. A heuristic iterative estimation-assignment algorithm is proposed for solving the bi-level optimization problem. Numerical examples are presented to illustrate the applications of the proposed model for assessment of network performance over the whole year.     

Equilibria of bilateral taxi–customer searching and meeting on networks

This paper proposes an equilibrium model to characterize the bilateral searching and meeting between customers and taxis on road networks. A taxi driver searches or waits for a customer by considering both the expected searching or waiting time cost and ride revenue, and a customer seeks a taxi ride to minimize full trip price. We suppose that the bilateral taxi–customer searching and meeting occurs anywhere in residential and commercial zones or at prescribed taxi stands, such as an airport or a railway station. We propose a meeting function to spell out the search and meeting frictions that arise endogenously as a result of the distinct spatial feature of the area and the taxi–customer moving decisions. With the proposed meeting function and the assumptions underlying taxi–customer search behaviors, the stationary competitive equilibrium achieved at fixed fare prices is determined when the demand of the customers matches the supply of taxis or there is market clearing at the prevailing searching and waiting times in every meeting location. We establish the existence of such an equilibrium by virtue of Brouwer’s fixed-point theorem and demonstrate its principal operational characteristics with a numerical example.     

Data Review of Transportation Infrastructure Public–Private Partnership: A Meta-Analysis

The limited understanding of public–private partnerships (P3s) for transportation infrastructure finance has been generally attributed to a lack of data. The more fundamental question of how P3 data are utilized in the literature is more relevant and critical, but remains unclear. This study investigates this question by examining the linkages between research objectives and data characteristics through a meta-analysis of infrastructure P3 studies using multinomial regressions. It analyzes 95 empirical studies that adopt actual data, selected from a P3 research database that includes over 345 studies and are classified into five categories including performance, contract, risk, value for money, and institutional factors. Results show that the case studies are less frequently utilized to understand P3s' institutional issues compared to those that focus on P3s' performance or VFM. Survey data are more frequently used to study P3 contracts rather than issues related to P3 project risk. We highlight the need for policy-makers to require continuing disclosure of P3 performance for validating the effectiveness of the procurement model and to improve the practice.     

Check-in allocation improvements through the use of a simulation–optimization approach

The aeronautical industry is still under expansion in spite of the problems it is facing due to the increase in oil prices, limited capacity, and novel regulations. The expansion trends translate into problems at different locations within an airport system and are more evident when the resources to cope with the demand are limited or are reaching to theirs limits. In the check-in areas they are appreciated as excessive waiting times which in turn are appreciated by the customers as bad service levels. The article presents a novel methodology that combines an evolutionary algorithm and simulation in order to give the best results taking into account not only the mandatory hard and soft rules determined by the internal policies of an airport terminal but also the quality indicators which are very difficult to include using an abstract representation. The evolutionary algorithm is developed to satisfy the different mandatory restrictions for the allocation problem such as minimum and maximum number of check-in desks per flight, load balance in the check-in islands, opening times of check-in desks and other restrictions imposed by the level of service agreement. Once the solutions are obtained, a second evaluation is performed using a simulation model of the terminal that takes into account the stochastic aspects of the problem such as arriving profiles of the passengers, opening times physical configurations of the facility among other with the objective to determine which allocation is the most efficient in real situations in order to maintain the quality indicators at the desired level.     

Recognition and tracking of spatial–temporal congested traffic patterns on freeways

The two models FOTO (Forecasting of Traffic Objects) and ASDA (Automatische Staudynamikanalyse: Automatic Tracking of Moving Traffic Jams) for the automatic recognition and tracking of congested spatial–temporal traffic flow patterns on freeways are presented. The models are based on a spatial–temporal traffic phase classification made in the three-phase traffic theory by Kerner. In this traffic theory, in congested traffic two different phases are distinguished: “wide moving jam” and “synchronized flow”. The model FOTO is devoted to the identification of traffic phases and to the tracking of synchronized flow. The model ASDA is devoted to the tracking of the propagation of moving jams. The general approach and the different extensions of the models FOTO and ASDA are explained in detail. It is stressed that the models FOTO and ASDA perform without any validation of model parameters in different environmental and traffic conditions. Results of the online application of the models FOTO and ASDA at the TCC (Traffic Control Center) of Hessen near Frankfurt (Germany) are presented and evaluated.