Cycling & Urban Commuting: Results of Behavioural Mode and Route Choice Models

The research reported here was undertaken as part of an Economic and Social Research Council Project (R000237103) entitled Cycling and Urban Mode Choice. The scope of the study is entirely commuting within an urban context. The objectives of the study were: • to better understand the interactions between car, bus, cycle and walk in an urban context and to explain observed variations in cycle trip rates across towns. • to be able to forecast the effect of a range of improvements to cycling facilities on mode choice • to use the estimated models to evaluate some actual schemes • to provide end users (eg consultants, local authorities, cycling organisations) with a tool to determine the effect of policy measures. In particular, to create models which will enhance the performance of strategic, integrated transport studies. The objectives of this paper are: • to describe the methodology that was used to examine cycling within an urban mode choice context • to outline the collection of data required to estimate behavioural models • to report the results of the estimated mode and route choice models • to provide illustrations of how the model can be used to appraise improvements to cycle facilities and cycling conditions. The structure of this paper is as follows. Section 2 gives a brief overview of the policy context of work on cycling in the UK and previous work in the area. Section 3 describes the methodology used to collect the Revealed Preference (RP) and Stated Preference (SP) data necessary for the model. Section 4 describes the data that was collected. Section 5 gives details of the development of the model and some empirical results from the model that was developed. Section 6 illustrates how the model could be used to appraise improvements in cycle facilities and cycling conditions. Section 7 draws some conclusions from the work

Unveiling E-bike potential for commuting trips from GPS traces

Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips

Factors influencing the propensity to cycle to work

This paper describes the development of a mode choice model for the journey to work with special emphasis on the propensity to cycle. The model combines revealed preference (RP) and stated preference (SP) data to form a very large and comprehensive model. RP data from the National Travel Survey was combined with a specially commissioned RP survey. A number of SP surveys were also undertaken to examine the effects of different types of en-route and trip end cycle facilities and financial measures to encourage cycling. The development of the model is described in detail. The model was used to forecast trends in urban commuting shares over time and to predict the impacts of different measures to encourage cycling. Of the en-route cycle facilities, a completely segregated cycleway was forecast to have the greatest impact, but even the unfeasible scenario of universal provision of such facilities would only result in a 55% increase in cycling and a slight reduction in car commuting. Payments for cycling to work were found to be highly effective with a £2 daily payment almost doubling the level of cycling. The most effective policy would combine improvements in en-route facilities, a daily payment to cycle to work and comprehensive trip end facilities and this would also have a significant impact on car commuting

Bicycle traffic and its interaction with motorized traffic in an agent-based transport simulation framework

Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to be able to model bicycle traffic more realistically. The network generation procedure is enriched to include attributes that are relevant for cyclists (e.g. road surfaces, slopes). Travel speed computations, plan scoring, and routing are enhanced to take into account these infrastructure attributes. The scoring, i.e. the evaluation of simulated daily travel plans, is furthermore enhanced to account for traffic events that emerge in the simulation (e.g. passings by cars), which have an additional impact on cyclists’ decisions. Inspired by an evolutionary computing perspective, a randomizing router was implemented to enable cyclists to find realistic routes. It is discussed in detail why this approach is both feasible in practical terms and also conceptually consistent with MATSim’s co-evolutionary simulation approach. It is shown that meaningful simulation results are obtained for an illustrative scenario, which indicates that the developed methods will make real-world scenarios more realistic in terms of the representation of bicycle traffic. Based on the exclusive reliance on open data, the approach is spatially transferable

Gartcosh Steel Mill regeneration project

New hi-tech, innovative businesses are springing up where Scotland's heavy industries once reigned supreme. Redevelopment of the former Ravenscraig steel works in Lanarkshire is continuing apace with a new town centre, complete with commercial facilities, soon to be constructed. The site of the former Gartcosh Steel Mill, also in Lanarkshire, is also being transformed, and architects and engineers from the University of Strathclyde are driving forward the plans. Gartcosh Business Interchange is set to become a new 50-hectare business location which has already been selected as the location for the major new office campus for Scotland's new FBI-style crime unit.It is anticipated that Gartcosh will create up to 170,000 sq ft of business space which could support up to 4000 jobs

Landscape Urban Structure Design - S. Romão Sportive Park, Leiria Polis, Portugal

It is due to the modern movement the loss of both landscape and open spaces multifunctionality. Consequently, it merges the term of “green spaces” amorphous and residual, often void and without any appropriation, so characteristic of the contemporary city. This study is a reflexion about and a practice result of the return to these spaces multifunctionality through a landscape structure on the urban space. We want this structure to be continuous, structuring and assuring biologic processes and fluxes that occur in the landscape systems. We present the casestudy of S. Romão Sportive Park included in Polis Program of Leiria City, in Portugal. It is a system of open spaces that constitutes itself as a landscape structure, continuous and multifunctional

The River Lune fact file

This document provides a brief introduction to the River Lune catchment and the role that the National Rivers Authority plays in catchment management. Included are a map of the catchment and short introductions to fisheries and characteristics of the catchment