https://nacto.org/wp-content/uploads/2016/02/2016_Fishman_Bikeshare-A-Review-of-Recent-Literature.pdf

Bikeshare: A Review of Recent Literature

This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1131&context=ccee_pubs

Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data

This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.).

A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges

Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective

Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial–temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial–temporal demand coverage approach for optimizing the placement of ET charging stations in the space–time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial–temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting.

/pdf/optimizing-the-locations-of-electric-taxi-charging-stations-d8xa8p8l02.pdf

Optimizing the Locations of Electric Taxi Charging Stations: a Spatial-temporal Demand Coverage Approach

Growing concerns about environmental issues have led to the consideration of alternatives to current mobility. Electric mobility is one such alternative that is receiving a great deal of attention in Europe. In particular, a new legal framework for the introduction of an electric mobility system in Portugal has recently been set up by the government. A key issue in this system is recharging the batteries and, consequently, the location of charging stations. This paper presents a study on the location of electric-vehicle charging stations for an area of Lisbon, the capital city of Portugal, characterized with a strong concentration of population and employment. This type of area is appropriate for slow charging because vehicles stay parked for several hours within a 24-h period. The methodology used here is based on a maximal covering model to optimize the demand covered within an acceptable level of service and to define the number and capacity of the stations to be installed. The results clearly indicate...

/pdf/optimal-location-of-charging-stations-for-electric-vehicles-3bocs3y1qf.pdf

Optimal Location of Charging Stations for Electric Vehicles in a Neighborhood in Lisbon, Portugal:

The promotion of sustainable alternatives to motorized individual mobility has been seen in the past few decades as one of the cornerstones in a strategy to reduce the negative externalities related to the transportation sector. Bicycle sharing is increasingly popular as a sustainable transport system and the number of bike sharing schemes has grown significantly worldwide in recent years. One of the most important elements in implementation of these systems is the location of the stations. In fact the non-optimal locating of bike sharing compromises its success. Municipalities or public–private partnerships are mostly responsible for implementing bike-sharing schemes. The public investment in bicycle mobility (particularly bike-sharing) is complex because it is always subject to a budget. The main concern for public investment is to maximize the benefits through the design and implementation of bike-sharing systems. This work sets out a methodology to help with the decision-making of bike-sharing systems. The research work we present proposes using an optimization method to design the bike sharing system such that it maximizes the demand covered and takes the available budget as a constraint. It combines strategic decisions for locating bike-sharing stations and defining the dimension of the system (stations and number of bicycles) with operational decisions (relocating bicycles). As an outcome, the model determines the optimal location of the bicycle stations, the fleet size, the capacity of the stations and the number of bicycles in each station, considering an initial investment lower than the given budget. In addition, it balances the annual cost of the system and the revenue assuming a possible supplementary budget from the system provider to cover any loss resulting from the shortfall between its operating cost and the revenue from the subscription charges. A case study in Coimbra, Portugal, is presented and discussed.

Bike-sharing stations: A maximal covering location approach

Bicycle sharing systems demand

There is a growing effort on policies and projects to support a modal shift towards active transport modes, namely the bicycle. The selection of preferred routes, and consequently the implementation of cycle routes, are usually based on distance, time and safety, not on exposure to pollutants. This paper presents a methodology to compare alternative routes, considering not only journey distance and time but also cyclist effort and exposure to pollutants mainly from traffic emissions (that also relates to the cyclist effort). The main purpose of this work is to develop a simple tool that allows municipalities and end users to estimate the effort required and the exposure to pollutants on potential routes, therefore supporting planning and design of infrastructure. The concept of cyclist specific effort (CSE) is developed in this work. The cyclist's highest specific ventilation rates are estimated from a direct relationship with the CSE, based on the specific characteristics of the users and on a calibrated relationship in the lab. A case study is presented for the route that connects two campuses of the University of Coimbra, Portugal (≈5 km). Results show that some alternative routes, which are generally not considered due to the increment of distance and/or time, can significantly reduce exposure to pollutants while requiring the same or even less effort. A reduction of approximately one third in the inhalation of pollutants from traffic emissions (33.1%) is possible by choosing a slightly longer route (6%) and requires a similar total effort.

Inês Frade

Papers

Optimal Location of Charging Stations for Electric Vehicles in a Neighborhood in Lisbon, Portugal:

Bike-sharing stations: A maximal covering location approach

Bicycle sharing systems demand

Energy consumption and pollutant exposure estimation for cyclist routes in urban areas

Bike sharing systems implementation impact on emissions, for cyclist preferred routes in urban areas