On the importance of demand consolidation in Mobility on Demand

International audienceMobility on Demand (MoD) services, like Uber and Lyft, are revolutionizing the way people move in cities around the world and are often considered a convenient alternative to public transit, since they offer higher Quality of Service (QoS-less waiting time, door-to-door service) at a cheap price. In the next decades, these advantages are expected to be further amplified by Automated MoD (AMoD), in which drivers will be replaced by automated vehicles, with a big gain in terms of cost-efficiency. MoD is usually intended as a door-to-door service. However, there has been recent interest toward consolidating, e.g., aggregating, the travel demand by limiting the number of admitted stop locations. This implies users have to walk from/to their intended origin/destination. The contribution of this paper is a systematic study the impact of consolidation on the operator cost and on user QoS. We introduce a MoD system where pickups and drop-offs can only occur in a limited subset of admitted stop locations. The density of such locations is a system parameter: the less the density, the more the user demand is consolidated. We show that, by decreasing stop density, we can increase system capacity (number of passengers we are able to serve). On the contrary, increasing it, we can improve QoS. The system is tested in AMoDSim, an open-source simulator. The code to reproduce the results presented here is available on-line. This work is a first step toward flexible mobility services that are able to autonomously re-configure themselves, favoring capacity or QoS, depending on the amount of travel demand coming from users. In other words, the services we envisage in this work shift their operational mode to any intermediate point in the range from a taxi-like door-to-door service to a bus-like service, with few served stops and more passengers on-board

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

EXPLOITING AVAILABLE URBAN TRANSPORTATION RESOURCES WITH TAXI SHARING AND RAPID TRANSPORTATION NETWORKS: A CASE STUDY FOR MILAN

We assess a bimodal transportation system based on a massive urban on-demand transportation service, named Taxi Sharing, and a rapid Local Public Transportation optimized for users without movement impairments, according to users' traveling and walking time. The aim is to increase, qualitatively and quantitatively, public mobility services by exploiting available urban transportation resources, in order to reduce private motorized mobility and related externalities in urban context. We developed a new technique to optimize a high quality Taxi Sharing service starting from state-of-the-art DARP optimization algorithms. In Taxi Sharing, time windows on pick-up and delivery times are narrow and the service is provided by many small vehicles, taxis. These features allow an enumeration of all possible subsets of incoming users' requests for each vehicle and to compute in real time an optimal set of routes by solving a large set partitioning problem with state-of-the-art integer linear programming solvers. Owing to this fast global optimization capability, the system allows for a high quality service without any need of booking the ride in advance. We present three development scenarios according to demand level, we discuss the performance of the service in terms of number of requests serviced per hour, average travel time and waiting time, number of taxis simultaneously on duty, ride fare and taxi revenue. We explored the possibility of planning, in presence of Taxi Sharing, a rapid LPT optimized for users without movement impairments according to users' traveling and walking time. We based the optimization process on data collected in the field. We evaluated the effects of optimal stops spacing on commercial speed, in relation also to traffic light priority. Obtained results indicate a huge potential increase in efficiency related both to taxi service and to local public transportation

Solving transportation scheduling problems : models and algorithms

Given the increasing load on current logistics and transportation systems, it is crucial to improve resource utilization and reduce operational costs. This can be achieved by developing better models and algorithms for transportation planning and scheduling. The main challenges include the mathematical modeling of operational rules, uncertainties in operations, and large-scale problem size. This dissertation addresses crew scheduling in freight railways and vehicle routing problems (VRP) for mail processing and distribution centers (P&DCs). Our goal is to develop models and algorithms that improve efficiency and reduce operating costs. In Chapter 2, we propose an optimization model to support real-time freight railway crew assignment decisions. Due to workload balance requirements and operating regulations, the optimization model is difficult to solve for realistic instances. Hence, we propose model improvements and develop effective solution techniques to find optimal or near-optimal solutions very quickly. Chapter 3 extends the freight rail crew scheduling problem by incorporating uncertainty in train arrival and departure times. We propose a stochastic programming model, but this model is solvable only the number of scenarios is small. As a consequence, we develop heuristics that use an analytical model to calculate the expected total cost of a given choice of crew deadheads. Using this cost evaluator, we develop four local search based heuristic algorithms to sequentially improve crew scheduling decisions under uncertainty. In Chapter 4, we first cluster the pickup and drop off points in mail P&DCs into zones and then minimize the number of vehicles required and the total distance traveled to meet daily transport demand. The clustering is performed with a greedy randomized adaptive search procedure, and two heuristics are developed to find solutions to the VRP, which proved intractable for realistic instances. The heuristics are optimization-based within a rolling horizon framework. An extensive analysis is undertaken to evaluate the relative performance of the two heuristics. The contributions of this dissertation include modeling, algorithmic development, computational testing, and validation using real and randomly generated data.Mechanical Engineerin

ALGORITHMS FOR OPTIMIZATION PROBLEMS WITH FRACTIONAL RESOURCES

We consider a class of optimization problems having a distinctive feature: both discrete and continuous decisions need to be taken simultaneously. These problems arise in many practical applications, for example broadband telecommunications and green transportation problems, where resources are available, that can be fractionally consumed or assigned. These problems are proven of being harder than their purely discrete counterpart. We propose effective methodologies to tackle them. Our approach is to consider variants of classical combinatorial optimization problems belonging to three domains: packing, routing and integrated routing/packing. Our results suggest that indeed effective approaches exist, reducing the computational effort required for solving the problem. Mostly, they are based on exploiting the structure of optimal solutions to reduce the search space