Cyclists' Perception of Maintenance and Operation of Cycling Infrastructure—Results From a Norwegian Survey

The Norwegian authorities want to limit the extent of car use in city areas to existing levels. Such a limitation would help combat climate change, improve health of citizens, and alleviate congestion. This implies that any further increase in transport needs will have to be met by walking, cycling and use of public transport. Reaching this ambitious goal requires knowledge about cyclists' preferences concerning operation and maintenance (M&O) of roads and foot/cycle paths. Previous research suggests that M&O have great implications for travel mode choice, bicycle route/path choice, safety, security, and comfort. With the need to serve bicyclists of all ages and genders, this study additionally explores which M&O of roads and foot/cycle the different demographic groups perceive positively or negatively. This article reports results from a nationwide survey in the summer of 2019. Two thousand three hundred seventy-six cyclists across Norway (55% male; 29% <40; 17% >60) participated to determine the cyclists' perceptions about year-round M&O of roads and foot/cycle paths. Respondents, rather than being randomly selected, completed an internet-linked survey. The variables included maintenance of foot/cycle paths in terms of salt and snow plowing and operation and maintenance of roads in terms of glass, holes/bumps, and conditions. Our results suggest that female cyclists suffer more from adverse conditions than do males. We also find that males are more likely to cycle during winter, which is an additional indication that adverse conditions affect women and men differently. Surprisingly, older cyclists report to be less affected by poor conditions than younger cyclists. Self-selection to participate in the survey among older cyclists might be an important explanation for this result. Cycling conditions vary greatly between geographical areas, reflecting the large climatic variations across Norway. Most respondents have experienced a cycle accident where conditions contributed, and many sometimes forfeit cycling due to adverse conditions. Implications for future research and practice of M&O are discussed.

Winter Road Surface Conditions Classification using Convolutional Neural Network (CNN): Visible Light and Thermal Images Fusion

In winter, road conditions play a crucial role in traffic flow efficiency and road safety. Icy, snowy, slushy, or wet road conditions reduce tire friction and affect vehicle stability which could lead to dangerous crashes. To keep traffic operations safe, cities spend a significant budget on winter maintenance operations such as snow plowing and spreading salt/sand. This paper proposes a methodology for automated winter road surface conditions classification using Convolutional Neural Network and the combination of thermal and visible light cameras. As part of this research, 4,244 pairs of visible light and thermal images are captured from pavement surfaces and classified into snowy, icy, wet, and slushy surface conditions. Two single-stream CNN models (visible light and thermal streams), and one dual-stream CNN model are developed. The average F1-Score of dual-stream model is 0.866, 0.935, 0.985, and 0.888 on snowy, icy, wet, and slushy, respectively. The weighted average F1-Score is 0.94.

Optimized Route to Clear Diverging Diamond Interchange Using Discrete Optimization Method

Interchanges and intersections are the most complex part of a roadway network and are very challenging for snow plowing operators. The objective of the study is to test if the empirical best plowing route to clear a Diverging Diamond Interchange (DDI) recommended by Clear Roads is also mathematically optimized. A discrete optimization method was employed to find the shortest route. In the study, the DDI is represented as a directed graph model. The task of clearing all lanes is treated as the well-known directed Chinese postman problem, which was then solved by an existing network optimization algorithm upon appropriate modification. The results showed that the best practice plowing route recommended by Clear Roads is one of the computed optimal routes with the Efficiency Index of 2/3. The approach proposed in the study can also be applied to other complex intersections and interchanges and help agencies achieve cost-effective snow control operations.

A case study of combined winter road snow plowing and de-icer spreading

In this article, we address a winter maintenance problem where the streets need to be plowed and gritted in a sequence that depends on the class of the road. The maintenance fleet includes vehicles equipped for plowing, some for spreading, and some for both at once. The objective is to complete the operations as rapidly as possible while considering street hierarchy, turn restrictions, heterogeneous speeds, and street–vehicle compatibility. An adaptive large neighborhood search framework is developed to solve the problem. Analysis of the results obtained can provide both a good basis for vehicle routing and help managers plan long-term policies and investments.