Movement-based Group Awareness with Wireless Sensor Networks

We propose a method through which dynamic sensor nodes determine that they move together, by communicating and correlating their movement information. We describe two possible solutions, one using inexpensive tilt switches, and another one using low-cost MEMS accelerometers. We implement a fast, incremental correlation algorithm, with an execution time of 6ms, which can run on resource constrained devices. The tests with the implementation on real sensor nodes show that the method is reliable and distinguishes between joint and separate movements. In addition, we analyze the scalability from four different perspectives: communication, energy, memory and execution speed. The solution using tilt switches proves to be simpler, cheaper and more energy efficient, while the accelerometer-based solution is more reliable, more robust to sensor alignment problems and, potentially, more accurate by using extended features, such as speed and distance

Sharing Mobility Data for Planning and Policy Research

A California Public Utilities Commission (CPUC) rulemaking and possible legislative action in 2020 could affect data sharing requirements, with implications for shared mobility providers. The purpose of this brief is to inform this regulatory and legislative decision-making. We solicited policy and planning questions and data needs for shared mobility from within the University of California Institute of Transportation Studies research network. We defined shared mobility as including shared mobility devices, such as e-bikes and e-scooters, and transportation network companies (TNCs). We evaluated whether data shared in accordance with each of six mobility data specifications could be used to support analyses that would answer these questions. We then defined three approaches to data sharing and analysis to address these and other questions, presenting the advantages and disadvantages of each. This brief does not address the full breadth of the questions raised in the CPUC rulemaking nor does it introduce the complexities of this topic. Beyond the scope of this brief are issues of user privacy, the legal authority for sharing data, and contractual or requirements for each possible model of data sharing and analysis

Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data

The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values

User producer interaction in context: a classification

Science, Technology and Innovation Studies show that intensified user producer interaction (UPI) increases chances for successful innovations, especially in the case of emerging technology. It is not always clear, however, what type of interaction is necessary in a particular context. This paper proposes a conceptualization of contexts in terms of three dimensions – the phase of technology development, the flexibility of the technology, and the heterogeneity of user populations – resulting in a classification scheme with eight different contextual situations. The paper identifies and classifies types of interaction, like demand articulation, interactive learning, learning by using and domestication. It appears that each contextual situation demands a different set of UPI types. To illustrate the potential value of the classification scheme, four examples of innovations with varying technological and user characteristics are explored: the refrigerator, clinical anaesthesia, video cassette recording, and the bicycle. For each example the relevant UPI types are discussed and it is shown how these types highlight certain activities and interactions during key events of innovation processes. Finally, some directions for further research are suggested alongside a number of comments on the utility of the classification

Trials with Microwave Detection of Vulnerable Road Users and Preliminary Empirical Modal Test. DRIVE Project V1031 Deliverable 11.

The general objective of the project is to provide a set of tools for the creation of traffic systems that enhance the safety and mobility of vulnerable road users (VRUs). This is being achieved in two ways: 1. By evaluating a number of RTI applications in signalling and junction control, in order to ascertain what benefits can be obtained for vulnerable road users by such local measures. 2. By developing a model of the traffic system that incorporates vulnerable road users as an integral part. The present workpackage, one of the last ones within the project, is intended to link the two strands together. The workpackage consists of two main parts: 1. Experiments with pedestrians and bicyclists. Two experiments were carried out, one in England (Bradford) and one in Sweden (Vijrjo), both applying microwave detectors for detection of pedestrians in a signalized intersection, but applying the detection in different ways. An observational study was carried out in Groningen (the Netherlands) to analyze bicycle/car interactions at an intersection with a cycle path. The aim of the experiment was to test the usefulness of a system giving car drivers warning in situations when a bicyclist approaches an intersection on a parallel bicycle path. 2. Reliability and validity testing of the submodels of the VRU-oriented traffic model WLCAN

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

Influence of Course Type on Upper Body Muscle Activity in Elite Cross-Country and Downhill Mountain Bikers During Off Road Downhill Cycling

This study aimed to investigate upper body muscle activity using surface electromyography (sEMG) in elite cross-country (XCO) and downhill (DH) cyclists during off road descending and the influence of man-made (MM) and natural terrain (NT) descents on muscle activity. Twelve male elite mountain bikers (n=6 XCO; age 23 ± 4 yrs; stature 180.5 ± 5.6 cm; body mass 70.0 ± 6.4 kg and n=6 DH; age 20 ± 2 yrs; stature 178.8 ± 3.1 cm; body mass 75.0 ± 3.0 kg) took part in this study. sEMG were recorded from the left biceps brachii, triceps brachii, latissimus dorsi and brachioradialis muscles and expressed as a percentage of maximal voluntary isometric contraction (% MVIC). Both groups performed single runs on different MM and NT courses specific to their cycling modality. Significant differences in mean % MVIC were found between biceps brachii and triceps brachii (p=.016) and triceps brachii and latissimus dorsi (p=.046) during MM descents and between biceps brachii and triceps brachii (p=.008) and triceps brachii and latissimus dorsi (p=.031) during NT descents within the DH group. Significant differences in mean % MVIC were found between biceps brachii and brachioradialis (p=.022) for MM runs and between biceps brachii and brachioradialis (p=.013) for NT runs within the XCO group. Upper body muscle activity differs according to the type of downhill terrain, and appears to be specific to DH and XCO riders. Therefore, the discipline specific impact on muscle activation and the type of course terrain ridden should be considered when mountain bikers engage in upper body conditioning programmes

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations