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.     

The Hamilton–Jacobi partial differential equation and the three representations of traffic flow

This paper applies the theory of Hamilton–Jacobi partial differential equations to the case of first-order traffic flow models. The traffic flow surface is analyzed with respect to the three 2-dimensional coordinate systems arising in the space of vehicle number, time and distance. In each case, the solution to the initial and boundary value problems are presented. Explicit solution methods and examples are shown for the triangular flow-density diagram case. This unveils new models and shows how a number of existing models are cast as special cases.     

Non‐physical barriers to traffic flow and the PTA programme of action: problems and prospects

A lot of resources have .been committed to the provision of transport infrastructure in Eastern and Southern Africa. Some of these resources have been borrowed from outside and have to be repaid. Hence the need to maximize their use. However, this is being minimized by non‐infrastructural bottlenecks to traffic flow. These range from macro‐economic constraints with implications for passenger (bus) transport such as the non‐availability or acute shortage of foreign exchange to slow, cumbersome documentation procedures particularly for cross‐border traffic.

The Preferential Trade Area (PTA) for Eastern and Southern Africa is geared to the reduction and eventual elimination of actual and potential non‐physical barriers to traffic flow, for which it has instituted a number of schemes. After introductory remarks on the sub‐regional economy, its transport sector and avenues for sub‐regional cooperation and the implementation of these schemes is assessed and other non‐physical barriers yet to be addressed by the PTA are examined before conclusions are drawn.     

On allowing a general form for unobserved heterogeneity in the multiple discrete–continuous probit model: Formulation and application to tourism travel

This paper proposes a new econometric formulation and an associated estimation method for a finite discrete mixture of normals (FDMN) version of the multiple discrete–continuous probit (MDCP) model. To our knowledge, this is the first such formulation and application of an MDCP model in the econometric literature. Using the New Zealand Domestic Travel Survey data set, the model is applied to analyze individual-level decisions regarding recreational destination locations and the number of trips to each destination. The results provide insights into the demographic and other factors that influence individuals’ preferences for different destinations, and show that the FDMN MDCP model is able to identify different segments of the sample, each one of them with different effects of the exogenous variables on destination choice.     

International experience with speed limits during and prior to the energy crisis of 1973–4

This paper presents an artificial neural network (ANN) based method for estimating route travel times between individual locations in an urban traffic network. Fast and accurate estimation of route travel times is required by the vehicle routing and scheduling process involved in many fleet vehicle operation systems such as dial‐a‐ride paratransit, school bus, and private delivery services. The methodology developed in this paper assumes that route travel times are time‐dependent and stochastic and their means and standard deviations need to be estimated. Three feed‐forward neural networks are developed to model the travel time behaviour during different time periods of the day‐the AM peak, the PM peak, and the off‐peak. These models are subsequently trained and tested using data simulated on the road network for the City of Edmonton, Alberta. A comparison of the ANN model with a traditional distance‐based model and a shortest path algorithm is then presented. The practical implication of the ANN method is subsequently demonstrated within a dial‐a‐ride paratransit vehicle routing and scheduling problem. The computational results show that the ANN‐based route travel time estimation model is appropriate, with respect to accuracy and speed, for use in real applications.     

Adding a new station and a road link to a road–rail network in the presence of modal competition

In this paper we study the problem of locating a new station on an existing rail corridor and a new junction on an existing road network, and connecting them with a new road segment under a budget constraint. We consider three objective functions and the corresponding optimization problems, which are modeled by means of mixed integer non-linear programs. For small instances, the models can be solved directly by a standard solver. For large instances, an enumerative algorithm based on a discretization of the problem is proposed. Computational experiments show that the latter approach yields high quality solutions within short computing times.     

Modelling contact mode and frequency of interactions with social network members using the multiple discrete–continuous extreme value model

Communication patterns are an integral component of activity patterns and the travel induced by these activities. The present study aims to understand the determinants of the communication patterns (by the modes face-to-face, phone, e-mail and SMS) between people and their social network members. The aim is for this to eventually provide further insights into travel behaviour for social and leisure purposes. A social network perspective brings value to the study and modelling of activity patterns since leisure activities are influenced not only by traditional trip measures such as time and cost but also motivated extensively by the people involved in the activity. By using a multiple discrete-continuous extreme value model (Bhat, 2005), we can investigate the means of communication chosen to interact with a given social network member (multiple discrete choices) and the frequency of interaction by each mode (treated as continuous) at the same time. The model also allows us to investigate satiation effects for different modes of communication. Our findings show that in spite of people having increasingly geographically widespread networks and more diverse communication technologies, a strong underlying preference for face-to-face contact remains. In contrast with some of the existing work, we show that travel-related variables at the ego level are less important than specific social determinants which can be considered while making use of social network data.     

Towards a microeconomic framework for modelling the joint choice of activity–travel behaviour and ICT use

The rapid development of information and communication technologies (ICT) has been argued to affect time use patterns in a variety of ways, with consequent impacts on travel behaviour. While there exists a significant body of empirical studies documenting these effects, theoretical developments have lagged this empirical work and in particular, microeconomic time allocation models have not to date been fully extended to accommodate the implications of an increasingly digitised society. To address this gap, we present a modelling framework, grounded in time allocation theories and the goods–leisure framework, for joint modelling of the choice of mode of activity (physical versus tele-activity), travel mode and route, and ICT bundle. By providing the expression for a conditional indirect utility function, we use hypothetical scenarios to demonstrate how our framework can conceptualise various activity–travel decision situations. In our scenarios we assume a variety of situations such as the implications of severe weather, the introduction of autonomous vehicles, and the interaction between multiple decision makers. Moreover, our approach lays the microeconomic foundations for deriving subjective values of ICT qualities such as broadband speed or connection reliability. Finally, we also demonstrate the means by which our framework could be linked to various data collection protocols (stated preference exercises, diaries of social interactions, laboratory experiments) and modelling approaches (discrete choice modelling, hazard-based duration models).     

CO2 emissions: Are land-use changes enough for California to reduce VMT? Specification of a two-part model with instrumental variables

With vehicle miles of travel increasing at a faster pace than population, one strategy being actively pursued by both state and local governments is compact development. California recently passed legislation that aggressively promotes sustainability by endorsing and rewarding compact development. Likewise, the California Air Resources Board has set a statewide reduction target of 5MMT of greenhouse gas reductions from land use, based largely on achieving compact development patterns. In this paper, we use a multivariate two-part model with instrumental variables, which corrects for residential location self-selection bias. We use a much larger and more geographically representative travel survey on household travel patterns and socio-economic characteristics than represented in previous California studies; this allows us to robustly consider other influences on travel. Our results indicate that, all else equal, a 10% in residential density would reduce VMT by 1.9%. This elasticity is larger than the reported in previous econometric studies for the US, and specifically for California. However, as we show, the magnitude of this impact is still low considering reasonable ranges for policies aimed to increase residential density.     

Feasibility of using time–space prism to represent available opportunities and choice sets for destination choice models in the context of dynamic urban environments

H?gerstrand??s original framework of time geography and the subsequent time?Cspace prism computational methods form the foundation of a new computational method for potential path areas (PPA) in a realistic representation of dynamic urban environments. In this paper the time?Cspace prism framework is used to assess sensitivity of PPA size to different parameters and to build choice sets for regional destination choice models. We explain the implication of different parameters to choice set formation in a step-wise manner and illustrate not only the complexity of the idea and the high computational demand but also behavioral realism. In this context, this paper tests the feasibility of using constraint-based time?Cspace prism to find the choice sets for a large-scale destination choice model, and identifies a variety of implementation issues. Computational demand is estimated based on a household travel survey for the Southern California Association of Government, and the feasibility of using time?Cspace prisms for destination choice models is assessed with different levels of information on the network and destinations available. The implications of time of day effects and flexibility in scheduling on choice set development due to varying level of service on the network and availability of activity opportunities are discussed and numerically assessed.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.