Policy 2.0 Platform for Mobile Sensing and Incentivized Targeted Shifts in Mobility Behavior

Abstract

Sustainable mobility and smart mobility management play important roles in achieving smart cities' goals. In this context we investigate the role of smartphones as mobility behavior sensors and evaluate the responsivity of different attitudinal profiles towards personalized route suggestion incentives delivered via mobile phones. The empirical results are based on mobile sensed data collected from more than 3400 people's real life over a period of six months. The findings show which user profiles are most likely to accept such incentives and how likely they are to result in more sustainable mode choices. In addition we provide insights into tendencies towards accepting more sustainable route options for different trip purposes and illustrate smart city platform potential (for collection of mobility behavior data and delivery of incentives) as a tool for development of personalized mobility management campaigns and policies.

Integrating Big Data into a Sustainable Mobility Policy 2.0 Planning Support System

“…In this context, the present contribution formulates a pioneering proposal, by drawing a Policy 2.0 concept and exploring the real-life use case where Policy 2.0 platform is used in supporting sustainable mobility related planning. The idea of Policy 2.0 platform for smart cities has been already discussed (but only in a preliminary formation) in Semanjski et al [33]. In this paper, the authors significantly extend their previous contribution by detailing how data driven insights can contribute to decision making and smart city's planning processes based on a Policy 2.0 platform.…”

“…The acceptability of this incentive varied among different homogeneous subgroups of users, reaching up to 18% for Carfree Choosers [33]. Compared with results from literature where traditional communication means were used to familiarize transport system users with available alternatives [77] these results are on average twice higher.…”

“…Among all profiles, car oriented ones were the least sensitive towards the provided incentive as they mainly explored alternative car routes, which were shorter than the one they would usually take. More detailed description of the application, campaign, observed behavioural change and modal shifts per profiles can be found in our previous publication Semanjski et al [33] and the campaign's/application's web sites [45,52].…”

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

“…As a consequence, current systems measure air pollution at a very low spatial resolution, e.g., only 22 stations covering a 50 × 50 km 2 (113 km 2 per station) in Beijing, 14 stations covering 1572 km 2 (112 km 2 per station) in London, and 61 stations (221 km 2 per station) in Flanders, Belgium. Researchers have also placed pollution sensors to assess the personal exposure to air pollution in places of interest [6][7][8][9], such as major roads with heavy traffic and industrial sites. Measurements obtained at these stations or locations can only reflect the air pollution level there or in small areas around there.…”

Forecasting the State of Health of Electric Vehicle Batteries to Evaluate the Viability of Car Sharing Practices

“…In order to balance the need for personal mobility and increase the sustainability of mobility systems as a whole, much has been done in promoting active transport modes such as biking and walking. However, overall results are still unsatisfactory [2], and results seem to be limited, as many are not motivated by topics such as fitness or environment, and are continuing to use personal vehicles for their mobility needs even when other feasible solutions exist [3]. Secondly, studies have shown that personal vehicles are used on average around an hour per day [4,5].…”