Availability-based dynamic pricing on a round-trip carsharing service: an explorative analysis using agent-based simulation

Carsharing companies aim to customize their service to increase fleet usage and revenues with different pricing schemes and offer types. Dynamic pricing policies can be designed to adjust and balance temporally and spatially cars availability but may pose some question on customers’ fairness. In this paper, we propose an explorative analysis of how an availability-based dynamic pricing scheme impacts the demand and the supply performance. The policy is simulated in MATSim and compared to a fixed pricing policy scheme. This simulation consists of analyzing the behavior of a synthetic population of car-sharing members for Berlin and the surrounding region in which is applied an availability-based dynamic pricing in which price depends on vehicle availability in booking stations. Results show that when the dynamic pricing is applied there is a light decrease in the number of bookings and people with low value of time tend to abandon the carsharing mode in favor of other modes of transportation

Innovations in weather services as a crucial building block for climate change adaptation in road transport

The road transport sector is facing rising uncertainties in planning and operations due to climate change induced changes in weather variability and extreme events. However, because of the high level of uncertainty related to the future climate, adaptation measures should be robust so as to retain the option value of the portfolio of measures. As an example of such a measure, this paper evaluates how foreseen innovations in weather services could reduce weather sensitivity and, consequently reduce the negative effects of climate change in the sector. The study is based on a theoretical framework on climate change adaptation and valuation of weather and climate services using the Weather Service Chain Analysis. We apply these frameworks to the road transport sector with a special emphasis on drivers’ decision making before and during a trip. We show that improved weather information, including more accurate weather forecasts, new applications and information dissemination channels can decrease the vulnerability of the mode to projected shifts in extreme weather patterns due to climate change

Preferences for Alternative Fuel Trucks among International Transport Companies

ABSTRACT: Fossil-fuel consumption in land freight is over 19%. Alternative fuel trucks (AFTs) help decrease greenhouse gas emissions. However, AFT preferences in international land transit have received little attention due to passing through various countries with different financial and regulation plans. This variety affects AFTs’ market share. This study analyzes factors influencing AFT preferences in international land transit. A questionnaire (designed in four sections) was distributed among international transport companies in Iran and Europe. A principal component analysis helped extract principal components composed of cognitive, environmental, policy, practical, and economic variables. The multinomial logit models include 26 meaningful variables. The marginal effect analysis shows that the service quality of the manufacturer, importance of greenhouse gas mitigation, and social responsibility do not significantly elevate AFT preferences. In addition, cargo type influences AFT choice. The results of this study help to identify the barriers affecting AFTs’ market share, which can positively impact air pollution

Public transit route mapping for large-scale multimodal networks

ABSTRACT: For the simulation of public transport, next to a schedule, knowledge of the public transport routes is required. While the schedules are becoming available, the precise network routes often remain unknown and must be reconstructed. For large-scale networks, however, a manual reconstruction becomes unfeasible. This paper presents a route reconstruction algorithm, which requires only the sequence and positions of the public transport stops and the street network. It uses an abstract graph to calculate the least-cost path from a route's first to its last stop, with the constraint that the path must contain a so-called link candidate for every stop of the route's stop sequence. The proposed algorithm is implemented explicitly for large-scale, real life networks. The algorithm is able to handle multiple lines and modes, to combine them at the same stop location (e.g., train and bus lines coming together at a train station), to automatically reconstruct missing links in the network, and to provide intelligent and efficient feedback if apparent errors occur. GPS or OSM tracks of the lines can be used to improve results, if available. The open-source algorithm has been tested for Zurich for mapping accuracy. In summary, the new algorithm and its MATSim-based implementation is a powerful, tested tool to reconstruct public transport network routes for large-scale systems

Retailers location choice based on shopping and land prices

For the success of a retailer, stores location is considered as one of the most important factors. In the evaluation of a location, contextual to the opening or the relocation of a shop, different aspects should be considered like accessibility, land price, applicable prices in the area, competition, etc. The work reported in this paper extends a previous effort which integrated an agent-based module of retailers’ location decision into the multi-agent transport simulation (MATSim). The module tries to find a constellation of stores which increases the number of customers and turnover for the retailer, based on the different shopping and land prices in the simulated area. The functionality of the module has been demonstrated on the ”Greater-Zurich” scenario, where real grocery retail stores of the area are modeled. The results obtained show a significant increase in both number of customers and turnover. Finally, possible limitations and necessary improvements of the developed module are presented