Space is one of the most valuable assets in urban environments. Currently, many cities devote a significant amount of space to private, less sustainable modes of transport. Several attempts have been made to recover urban space for other purposes, e.g. pedestrians, low-speed residential areas, public modes of transport, or bike lanes. In this paper, we propose a new analytical methodology that quantifies

Weather is fundamentally a perception rather than an objective measure. This study uses data from a four-wave travel diary survey and aims to answer two research questions, i.e. 1. How individuals from different socio-demographic groups perceive weather. 2. How an individual’s weather perception affects his/her leisure activity participation decision. A thermal indicator, Universal Thermal Climate

The bus driver scheduling problems aim to optimally deploy drivers to fulfil published timetables for bus services subject to drivers’ contractual working rules. In this study, we first develop an integer linear programming model for a practical driver scheduling problem. We proceed to formulate another integer linear programming model for the driver scheduling enhancement to incorporate with uncertain

This study proposes a tensor-based K-Nearest Neighbors (K-NN) method, in which traffic patterns involve multi-dimensional temporal information and bi-directional spatial information. Such multi-temporal information can not only capture the instantaneous fluctuation of short-term traffic but keep the general trend of long-term traffic. In numerical experiments, with taxis’ GPS data from an urban road

It is foreseeable that connected vehicles (CVs) and regular vehicles (RVs) will be operated together on roads in the upcoming decades. The appearance of CVs provides us an opportunity to improve traffic efficiency by guiding drivers to properly steer their CVs. To take advantage of this opportunity, this paper proposes two cooperative driving strategies for CVs that move in heterogeneous RV and CV

Connected vehicles (CVs) are considered to have the potential to significantly improve traffic flow stability. Although several studies have been devoted to modelling car-following behaviour in a connected environment, most model formulations are based on assumptions without empirical observations. Therefore, this paper utilizes data from field experiments to explore the dynamics of CVs. Data mining

We study the dynamics of a class of bi-agent logistics systems consisting of two types of agents interacting on an arbitrary complex network. By approximating the system with simple microscopic models and solving them analytically, we reveal some universal dynamical features and propose applications of such features for system optimisations. Large-scale agent-based numerical simulations are also carried

The MDCEV modelling framework has established itself as the preferred method for modelling time allocation, with data very often collected through travel or activity diaries. However, standard implementations fail to recognise the fact that many of these datasets contain information on multiple days for the same individual, with possible correlations and substitution between days. This paper discusses

The one-way carsharing system has been widely used in the carsharing field due to its flexibility. However, one of its main disadvantages is the imbalance between supply and pickup demand. At present, multi-source data are available for the real-time prediction of pickup demand. The multi-source data that are used for this purpose include real-time user application log data, historical order data,

Lane changing is one of the basic driving behaviours, which may induce traffic oscillations and incidents. However, it is difficult to well model the lane-changing decision process due to the complex traffic status. To promote the prediction accuracy of lane-changing decisions, this paper presents a probability lane-changing model by taking into account the memory effect. That is, the lane-changing

An effective macroscopic lane change (LC) model is required to facilitate active and dynamic lane management to develop cell-based multilane macroscopic traffic models. Existing logistic regression LC models do not undertake model classification of lane change; do not consider performance measures in the validation of field data and ignore movement between lanes during time-varying traffic. Models

The dynamics of mixed traffic were analysed by examining the driving features of various vehicles based on their car-following behaviours in a test-driving circuit. By applying decision tree analysis to the observed data, we successfully compared the acceleration, velocity difference and distance gap of different vehicles during driving trials and quantitatively extracted the features of these physical

A motorway weaving section connects a pair of closely spaced entry- and exit-ramps, where intensive lane-changings of merging and diverging vehicles take place over a relatively short space. A detailed trajectory data reveal that a significant proportion of the lane changing at the weaving section exhibits group lane-changing behaviour, in the forms of a lane-changing platoon and simultaneous weaving

A dynamic macroscopic parking pricing model is developed to maximize the revenue for a city, while simultaneously minimizing the total cruising time on the network. The proposed responsive pricing scheme takes the parking search phenomenon into consideration. This means that the parking fee also changes in response to the number of searching vehicles, in addition to changes in response to the parking

This paper addresses the first-order extension of the Lighthill–Whitham– Richards (LWR) macroscopic traffic flow model. Although previous studies have focused on the fluid aspect of traffic flow, none have addressed the sensitivity of drivers to the number of free spaces within a certain distance ahead of the subject driver. To incorporate driver behavior, we used the number of free spaces ahead of