Road safety is one of the most important applications of vehicular networks. However, improving pedestrian safety via vehicle-to-pedestrian (V2P) wireless communication has not been extensively addressed. In this paper, our vision is to propose a method which enables development of V2P road safety applications via wireless communication and only utilizing the existing infrastructure and devices. As pedestrians' smartphones do not support the IEEE 802.11p amendment which is customized for vehicular networking, we have initiated an approach that utilizes cellular technologies. Study shows potential of utilizing 3G and LTE for highly mobile entities of vehicular network applications. In addition, some vehicles are already equipped with cellular connectivity but otherwise the driver's smartphone is used as an alternative. However, smartphone limited battery life is a bottleneck in realization of such pedestrian safety system. To tackle the energy limitation in smartphones, we employ an adaptive multi-level approach which operates in an energy-saving mode in risk-free situations but switches to normal mode as it detects a risky situation. Based on our evaluation and analysis, this adaptive approach considerably saves electrical energy and thus makes the cellular-based road-safety system practical.

Pedestrian detection using wireless communication complements sensor-based pedestrian detection in driverless and conventional cars. This fusion improves road-safety particularly in obstructed visibility and bad weather conditions. This paper seeks developing such wireless-based vehicle-to-pedestrian (V2P) collision avoidance using energyefficient methods and non-dedicated existing technologies namely smartphones (widespread among pedestrians and drivers), cellular network and cloud. Our road-safety mobile app can be set to driver mode or pedestrian mode. This app frequently sends vehicle and pedestrian geolocation data (beacons) to cloud servers. Cloud performs threat analysis and sends alerts to road users who are in risky situation. However, constant pedestrian-to-cloud (P2C) beaconing can quickly drain smartphone battery and make the system impractical. We employ adaptive multi-mode (AMM) approach built on situation-adaptive beaconing. AMM reduces power consumption using beacon rate control while it keeps the data freshness required for timely vehicle-to-pedestrian collision prediction. AMM runs on cloud servers and commands the mobile apps to change P2C beaconing frequency according to collision risk level from the surrounding vehicular traffic. City-scale mobility simulation demonstrates energy efficiency of our approach. We evaluate battery lifetime according to geolocational variations over the city map. Results show that road-safety system imposes a small mean overhead on smartphone battery's state-of-charge. Furthermore, our evaluation of computation and network load shows feasibility of running such road-safety systems on conventional cellular networks and cloud providers. We use server-side prototype experiment to estimate minimum cloud resources and cloud service costs needed to handle computation of city-scale geolocation data.

Automatic detection of public transport (PT) usage has important applications for intelligent transport systems. It is crucial for understanding the commuting habits of passengers at large and over longer periods of time. It also enables compilation of door-to-door trip chains, which in turn can assist public transport providers in improved optimisation of their transport networks. In addition, predictions of future trips based on past activities can be used to assist passengers with targeted information. This article documents a dataset compiled from a day of active commuting by a small group of people using different means of PT in the Helsinki region. Mobility data was collected by two means: (a) manually written details of each PT trip during the day, and (b) measurements using sensors of travellers' mobile devices. The manual log is used to cross-check and verify the results derived from automatic measurements. The mobile client application used for our data collection provides a fully automated measurement service and implements a set of algorithms for decreasing battery consumption. The live locations of some of the public transport vehicles in the region were made available by the local transport provider and sampled with a 30-second interval. The stopping times of local trains at stations during the day were retrieved from the railway operator. The static timetable information of all the PT vehicles operating in the area is made available by the transport provider, and linked to our dataset. The challenge is to correctly detect as many manually logged trips as possible by using the automatically collected data. This paper includes an analysis of challenges due to missing or partially sampled information in the data, and initial results from automatic recognition using a set of algorithms. Improvement of correct recognitions is left as an ongoing challenge.

Evaluating potential of shifting to low-carbon transport modes requires considering limited travel-time budget of travelers. Despite previous studies focusing on time-relevant modal shift, there is a lack of integrated and transferable computational frameworks, which would use emerging smartphone-based high-resolution longitudinal travel datasets. This research explains and illustrates a computational framework for this purpose. The proposed framework compares observed trips with computed alternative trips and estimates the extent to which alternatives could reduce carbon emission without a significant increase in travel time.. The framework estimates potential of substituting observed car and public-transport trips with lower-carbon modes, evaluating parameters per individual traveler as well as for the whole city, from a set of temporal and spatial viewpoints. The illustrated parameters include the size and distribution of modal shifts, emission savings, and increased active-travel growth, as clustered by target mode, departure time, trip distance, and spatial coverage throughout the city. Parameters are also evaluated based on the frequently repeated trips. We evaluate usefulness of the method by analyzing door-to-door trips of a few hundred travelers, collected from smartphone traces in the Helsinki metropolitan area, Finland, during several months. The experiment’s preliminary results show that, for instance, on average, 20% of frequent car trips of each traveler have a low-carbon alternative, and if the preferred alternatives are chosen, about 8% of the carbon emissions could be saved. In addition, it is seen that the spatial potential of bike as an alternative is much more sporadic throughout the city compared to that of bus, which has relatively more trips from/to city center. With few changes, the method would be applicable to other cities, bringing possibly different quantitative results. In particular, having more thorough data from large number of participants could provide implications for transportation researchers and planners to identify groups or areas for promoting mode shift. Finally, we discuss the limitations and lessons learned, highlighting future research directions.

Given the necessity to understand the modal shift potentials at the level of individual travel times, emissions, and physically active travel distances, there is a need for accurately computing such potentials from disaggregated data collection. Despite significant development in data collection technology, especially by utilizing smartphones, there are limited efforts in developing useful computational frameworks for this purpose. First, development of a computational framework requires longitudinal data collection of revealed travel behavior of individuals. Second, such a computational framework should enable scalable analysis of time-relevant low-carbon travel alternatives in the target region. To this end, this research presents an open-source computational framework, developed to explore the potential for shifting from private car to lower-carbon travel alternatives. In comparison to previous development, our computational framework estimates and illustrates the changes in travel time in relation to the potential reductions in emission and increases in physically active travel, as well as daily weather conditions. The potential usefulness of the framework was evaluated using long-term travel data of around a hundred travelers within the Helsinki Metropolitan Region, Finland. The case study outcomes also suggest that in several cases traveling by public transport or bike would not increase travel time compared to the observed car travel. Based on the case study results, we discuss potentially acceptable travel times for mode shift, and usefulness of the computational framework for decisions regarding transition to sustainable urban mobility systems. Finally, we discuss limitations and lessons learned for data collection and further development of similar computational frameworks.