The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.

Climate change 2014 - Mitigation of climate change

There has been a resurgence of interest in demand-responsive shared-ride systems, motivated by concerns for the environment and also new developments in technologies which enable new modes of operations. This paper surveys the research developments on the Dial-A-Ride Problem (DARP) since 2007. We provide a classification of the problem variants and the solution methodologies, and references to benchmark instances. We also present some application areas for the DARP, discuss some future trends and challenges, and indicate some possible directions for future research.

A survey of dial-a-ride problems: Literature review and recent developments

With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation industry. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensitive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform and many other factors. Diverse variables and factors in the system are strongly endogenous and interactively dependent. How to design and operate ridesourcing systems is vital—and challenging—for all stakeholders: passengers/users, drivers/service providers, platforms, policy makers, and the general public. In this paper, we propose a general framework to describe ridesourcing systems. This framework can aid understanding of the interactions between endogenous and exogenous variables, their changes in response to platforms’ operational strategies and decisions, multiple system objectives, and market equilibria in a dynamic manner. Under the proposed general framework, we summarize important research problems and the corresponding methodologies that have been and are being developed and implemented to address these problems. We conduct a comprehensive review of the literature on these problems in different areas from diverse perspectives, including (1) demand and pricing, (2) supply and incentives, (3) platform operations, and (4) competition, impacts, and regulations. The proposed framework and the review also suggest many avenues requiring future research.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5517&context=sis_research

Ridesourcing systems: A framework and review

The rise of research into shared mobility systems reflects emerging challenges, such as rising traffic congestion, rising oil prices and rising environmental concern. The operations research community has turned towards more sharable and sustainable systems of transportation. Shared mobility systems can be collapsed into two main streams: Those where people share rides and those where parcel transportation and people transportation are combined. This survey sets out to review recent research in this area, including different optimization approaches, and to provide guidelines and promising directions for future research. It makes a distinction between prearranged and real-time problem settings and their methods of solution, and also gives an overview of real-case applications relevant to the research area.

https://hal.archives-ouvertes.fr/hal-02014342/document

A survey of models and algorithms for optimizing shared mobility

This study analyzes the potential benefits and drawbacks of taxi sharing using agent-based modeling. New York City (NYC) taxis are examined as a case study to evaluate the advantages and disadvantages of ride sharing using both traditional taxis (with shifts) and shared autonomous taxis. Compared to existing studies analyzing ride sharing using NYC taxi data, our contributions are that (1) we proposed a model that incorporates individual heterogeneous preferences; (2) we compared traditional taxis to autonomous taxis; and (3) we examined the spatial change of service coverage due to ride sharing. Our results show that switching from traditional taxis to shared autonomous taxis can potentially reduce the fleet size by 59% while maintaining the service level and without significant increase in wait time for the riders. The benefit of ride sharing is significant with increased occupancy rate (from 1.2 to 3), decreased total travel distance (up to 55%), and reduced carbon emissions (up to 866 metric tonnes per day). Dynamic ride sharing, wich allows shared trips to be formed among many groups of riders, up to the taxi capacity, increases system flexibility. Constraining the sharing to be only between two groups limits the sharing participation to be at the 50–75% level. However, the reduced fleet from ride sharing and autonomous driving may cause taxis to focus on areas of higher demands and lower the service levels in the suburban regions of the city.

Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC

Dynamic taxi and ride sharing problems with Money as an Incentive are studied.Heuristics are proposed and compared with others achieving better results.In the dynamic problem our approach is based on fast computation of minimum paths.Simulations show that users can save up to 30% on shared trips compared to private ones. This paper deals with a combinatorial optimization problem that models situations of both dynamic ride-sharing and taxi-sharing. Passengers who want to share a taxi or a ride, use an app to specify their current location, destination and further information such as the earliest departure time, the latest arrival time and the maximum cost they are willing to pay for the ride. Car owners also specify their origin, destination, the leaving time and the maximum accepted delay. Taxi drivers report their location and the time they will start and end the service. All drivers need to define a price per kilometer. The problem is to compute routes, matching requests to vehicles in such a way that ride-sharing is allowed as long as some restrictions are satisfied, such as: the capacity of the vehicle, maximum trip cost of each passenger and maximum delay. The problem is dynamic since new requests arrive on-line and routes can be modified in order to attend them. To solve this dynamic problem, the day is divided in time periods. For each period, an instance of a static problem is created and solved by a greedy randomized adaptive search procedure (GRASP). Experiments with instances based on real data were made to evaluate the heuristics and the proposed method. In our simulations with taxis, passengers paid, on average, almost 30% less than they would pay on private rides.

Taxi and Ride Sharing

In this paper we study a dynamic problem of ridesharing and taxi sharing with time windows. We consider a scenario where people needing a taxi or interested in getting a ride use a phone app to designate their source and destination points in a city, as well others restrictions (such as maximum allowable time to be at the destination). On the other hand, we have taxis and people interested in giving a ride, with their current positions and also some constraints (vehicle capacity, destination, maximum time to destination). We want to maximize the number of shared trips: in the case of taxis, people going to close locations can share the costs of the trip, and in case of rides, the driver and passengers can share costs as well. This problem is dynamic since new calls for taxis or calls for rides arrive on demand. This gives rise to an optimization problem which we prove to be NP-Hard. We then propose heuristics to deal with it. We focus on the taxi sharing problem, but we show that our model is easily extendable to model the ridesharing situation or even a situation where there are both taxis and car owners. In addition, we present a framework that consists basically of a client application and a server. The last one processes all incoming information in order to match vehicles to passengers requests. The entire system can be used by taxi companies and riders in a way to reduce the traffic in the cities and to reduce the emission of greenhouse gases.

Dynamic taxi and ridesharing: a framework and heuristics for the optimization problem

O presente artigo tem por objetivo tratar da normatização no Brasil dos sistemas de autorregulação implementados nas atividades empresariais, dentre os quais se encontram o compliance e a governança corporativa. Será objeto de destaque o compliance e a governança enquanto procedimentos aptos a estabelecer regras para o cumprimento de preceitos legais, no âmbito da tutela do bem jurídico ambiental.  Por outro viés, também será investigado e discorrido sobre a responsabilização penal da pessoa jurídica, e a possibilidade de o compliance, a governança corporativa e outras práticas organizacionais atuarem como ferramentas de prevenção a criminalização da pessoa jurídica por crimes ambientais.

As novas práticas corporativas e o programa de compliance, como instrumentos eficazes de prevenção da resposabilização penal da pessoa jurídica, sob a ótica do bem jurídico ambiental

Douglas O. Santos

Papers

Taxi and Ride Sharing

Dynamic taxi and ridesharing: a framework and heuristics for the optimization problem

As novas práticas corporativas e o programa de compliance, como instrumentos eficazes de prevenção da resposabilização penal da pessoa jurídica, sob a ótica do bem jurídico ambiental