Predictive spatial network analysis for high-resolution transport modeling, applied to cyclist flows, mode choice, and targeting investment

Abstract

Betweenness is a measure long used in spatial network analysis (SpNA) to predict flows of pedestrians and vehicles, and more recently in public health research. We improve on this approach with a methodology for combining multiple betweenness computations using cross-validated ridge regression to create wide-scale, high-resolution transport models. This enables computationally efficient calibration of distance decay, agglomeration effects, and multiple trip purposes. Together with minimization of the Geoffrey E. Havers (GEH) statistic commonly used to evaluate transport models, this bridges a gap between SpNA and mainstream transport modeling practice. The methodology is demonstrated using models of bicycle transport, where the higher resolution of the SpNA models compared to mainstream (four-step) models is of particular use. Additional models are developed incorporating heterogeneous user preferences (cyclist aversion to motor traffic). Based on network shape and flow data alone the best model gives reasonable correlation against cyclist flows on individual links, weighted to optimize GEH (r 2 D 0.78, GEH D 1.9). As SpNA models use a single step rather than four, and can be based on flow data alone rather than demographics and surveys, the cost of calibration is lower, ensuring suitability for small-scale infrastructure projects as well as large-scale studies.

Using road class as a replacement for predicted motorized traffic flow in spatial network models of cycling

“…Model 3, therefore, offers an improvement on the performance of ref. 18 which achieved R 2 = 0.78 in the prediction of measured flows, and equals that study in the prediction of mode share.…”

“…This model made the simplifying assumption that cyclists travel from everywhere to everywhere subject to a maximum trip distance. Later work 18 managed to discard these assumptions, in their place incorporating agglomeration effects, multiple trip purposes, heterogeneous preferences of different classes of cyclist, and the deterring effects of traffic and slope on mode share, to obtain a cross-validated fit with coefficient of determination R 2 = 0.78 between modelled and measured cyclist flows. In the latter model, both mode and route choice are based on “cyclist-adjusted distance” i.e.…”

“…It is equally detailed as the cycling model, but takes a simpler approach, being in contrast to ref. 18 , univariate, single purpose and ignoring distance decay. Nonetheless, the vehicle sub-model typically considers trips of up to 30 km from the city centre, i.e.…”

“…In the current context, road class is taken to represent cyclists’ perceptions of different roads, based on behavioural expectations, motor vehicle traffic, road function, number of lanes and speed limit, the latter being indirectly related to the road capacity. Our contribution is to combine road class as a predictor of cyclist behaviour, with a spatial network analysis approach, to model cyclist flows and mode share, and compare results with existing models based on a vehicle traffic sub-model 17,18 . Results show comparable performance albeit with substantially reduced modelling effort.…”

Location Preferences of New Pedestrian Bridges Based on Multi-Criteria Decision-Making and GIS-Based Estimation

“…Indirectly it is possible to evaluate the benefits with the spatial syntax methods used to define the (Marshall, Gil, Kropf, Tomko, & Figueiredo, 2018;Parthasarathi & Levinson, 2018). A number of researches conducted on the theme of walking and space syntax demonstrate that the position of the bridge -the element of the street network -is the primary factor influencing pedestrian movement (Cooper, 2018;Koohsari, Oka, Owen, & Sugiyama, 2019;Pafka, Dovey, & Aschwanden, 2018;Suzuki, 2018). The space syntax characteristics of integration and connectivity measures positively influence pedestrian movement with the choice being the most reliable predictor (Sharmin & Kamruzzaman, 2018).…”