On-demand Transportation Systems Research Articles

Enhancing Service Quality of On-Demand Transportation Systems Using a Hybrid Approach with Customized Heuristics

As customers’ expectations continue to rise, advanced on-demand transport services face the challenge of meeting new requirements. This study addresses a specific transportation issue belonging to dial-a-ride problems, including constraints aimed at fulfilling customer needs. In order to provide more efficient on-demand transportation solutions, we propose a new hybrid evolutionary computation method. This method combines customized heuristics including two exchanged mutation operators, a crossover, and a tabu search. These optimization techniques have been empirically proven to support advanced designs and reduce operational costs, while significantly enhancing service quality. A comparative analysis with an evolutionary local search method from the literature has demonstrated the effectiveness of our approach across small-to-large-scale problems. The main results show that service providers can optimize their scheduling operations, reduce travel costs, and ensure a high level of service quality from the customer’s perspective.

On the planning of zone-based electric on-demand minibus

An on-demand transportation system can enhance traffic efficiency through its flexible services. In this study, we introduce a planning problem for an on-demand electric minibus service within urban areas. We are considering a zone-based operation, meaning that the on-demand minibus is restricted to picking up or dropping off passengers within a single zone for each trip, and the minibus does not stop in other zones. For this zonal electric minibus system, we plan the service level for each zone, the construction of dedicated charging piles in each zone, and the optimal size of the bus fleet. To model the planning problem, we propose a mixed-integer nonlinear second-order cone program. Additionally, we introduce an adaptive discretization algorithm to expedite the problem-solving process. Our numerical tests illustrate the planning for the on-demand minibus system in Manhattan. Furthermore, these tests underscore the benefits of the proposed on-demand minibus system. The most important findings of this study are: (i) the proposed mode replaces more trips with cars, thus reducing the traffic flow and alleviating traffic congestion; (ii) the service zone division, the setting of the fare, and the number of seats in minibus play an essential role in the revenue of the on-demand minibus system; (iii) considering endogenous zone selecting, number of dedicated charging piles, and mode choice in each zone is essential to the planning of the on-demand minibus system.

SAN ANTONIO TRANSPORTATION PLANNING SYSTEM: MAPPING INFRASTRUCTURE CAPACITY AND COORDINATION

Abstract Ride Connect Texas and its partnering agencies seek to develop an expanded senior mobility management system supported by a One Call One Click transportation planning system. Its purpose is to enhance on-demand transportation system coordination and the application of technology, providing consumers, service providers, and mobility managers with direct access to comprehensive transportation information. GIS mapping is used to help develop a spatial awareness of the transportation challenges among older people and individuals with a disability and the current system capacity. Interviews with transit, non-transit, and health and human service agencies will reveal factors that influence coordination among providers and opportunities for capacity expansion. Surveys and focus groups of older adults and individuals with disability will show differentiating characteristics of riders and transportation needs and challenges. GIS mapping will allow us to geocode these findings relative to the user’s street address and zip code, illustrating variations in specialized client needs, service availability, travel behavior, and available amenities for basic daily living. Mapping will identify gaps in transportation services, hot zones revealing high user demand and low system capacity, and the ability to reduce fragmentation of service through seamless technology-based coordination. The mapping element informs the measurement of a community’s transportation system capacity for this user group. The value proposition of this tool is its ability to help any community measure its readiness toward developing a comprehensive implementation strategy for coordinated demand response transportation, including resource mobilization strategies, capacity augmentation, phasing of the system, and implementation timeline.

Decentralized multi-agent approach based on A* algorithm for on-demand transport problem

The on-demand transport (ODT) systems have developed worldwide as they have significant social, environmental, and economic benefits. Even with those benefits, it’s still important to gain popular acceptance. The acceptance key is the reactivity of the system in providing fast and reliable solutions whilst respecting vehicles’ and clients’ constraints. This paper presents a decentralized multi-agent approach to model and solve the ODT problem in a static road network. The agents interact with each other using the A* algorithm to find an optimal solution for each transport demand. The optimal solution is expressed by the fastest trajectory taken by the cheapest vehicles. We utilize factual data from a Lebanese city to do experiments evaluating the proposed approach.

Simulative Study of an Innovative On-Demand Transport System Using a Realistic German Urban Scenario

Trams are a meaningful means of public transport in urban traffic. However, trams have some well-known disadvantages. These include, for example, possibly long distances to the stop, long waiting times, and lack of privacy, among others. The innovative mobility concept “FLAIT-Train” offers solutions to the problems mentioned. The FLAIT-train operates on ordinary roads and is intended to offer DOOR-2-DOOR transport. In the first application phase, the FLAIT-train runs on exclusive lanes but in the future can mix with other traffic. They are designed as vehicles with 2 seats and 1 m width. The vehicle technology of FLAIT-trains is similar/identical to battery-electric autonomous vehicles. This paper uses traffic simulations to investigate whether FLAIT trains are a suitable alternative to conventional trams, taking simulated/theoretical transport capacities in passenger-kilometers per day into account. Using the software SUMO (“Simulation of Urban Mobility”), a realistic traffic scenario is generated. In this scenario, the operation of the FLAIT-Trains and the trams are simulated under the same conditions and based on statistical data. Based on the simulation results, the performances of the FLAIT-Trains and the trams are compared.

Public Acceptance of Last-Mile Shuttle Bus Services with Automation and Electrification in Cold-Climate Environments

The last-mile shuttle bus service with automation and electrification has emerged to fill gaps in on-demand transportation systems and its goals are to satisfy the door-to-door mobility needs of residents. It could help to enhance the happiness of public travel in cold-climate environments, which is also considered a pro-social public transportation service. Although it has the potential to promote sustainable and environmentally friendly mobility systems, the successful implementation of last-mile shuttle bus services with automation and electrification highly depends on individuals’ willingness to accept. In this paper, a theoretical acceptance model for last-mile shuttle bus services with automation and electrification is proposed. Partial least squares structural equation modeling is employed to examine research model in accordance with 986 valid questionnaires answered by public in snow and ice environments. The outcomes show that the proposed model accounts for 73.4% of the variance in behavioral intention to utilize last-mile shuttle bus services with automation and electrification. The strongest determinants of behavior intention are attitude and perceived usefulness. In addition, perceived risk negatively affects behavioral intention. We also provide theoretical findings and practical suggestions for developing last-mile shuttle bus services with automation and electrification based on the results and our analysis.

A multi-agent approach for on-demand transportation problem in cities

On-demand transportation (ODT) systems have proliferated in diverse cities worldwide due to their social, economic and environmental advantages. Despite those advantages, it is vital to get public approval. The approval key is the system’s reactivity in supplying speedy and reliable solutions that consider clients’ and vehicles’ constraints. Those solutions have to reflect actual life conditions to optimize the quality of service. The most regarded challenge in studying the ODT problem in cities is the stochastic time-dependent travel speed that varies due to traffic fluctuations. To deal with an actual ODT problem, a system has to represent the traffic on its scale. Hence, estimating the travel speed at a specific time and affording a solution based on reliable traffic data. Accordingly, the passengers are served better. This work contributes to the study by solving the ODT problem in cities with a massive multi-agent system that considers historical traffic data and unpredictable events disrupting the typical traffic. We evaluate the proposed approach by experiments with instances based on actual data for a city in the north of Lebanon. The results reveal that the quality of service increases when the stochastic time-dependent travel speed is considered. 50% to 100% of affected clients by an unpredictable event are satisfied when this event is considered by the system.

Operational analysis of an innovative semi-autonomous on-demand transportation system

Semi-autonomous transportation systems are an intermediate step towards full automation of transportation systems. They use and benefit from the technological advances brought by automation while being already implementable at larger scale under the current regulations and within the existing urban environment.In this paper, we present a new type of semi-autonomous transportation system referred to as Multi-Layered Personal Transit System (MuLPeTS). It consists of convoys composed of one human-driven lead vehicle guiding several autonomous small capacity trailers in which the passengers travel. These trailers can detach from a convoy and travel autonomously in a protected environment before attaching later to another convoy. The interest of this transportation concept is threefold: (i) this assembly of vehicles is able and allowed to move in mixed-traffic conditions whereas fully autonomous driving is still largely restricted; (ii) a trailer can travel autonomously in the vicinity of stations to pick-up and drop-off passengers while the convoy it was previously part of continues its route without further delay; and (iii) passengers complete their entire journey on board a single trailer, that is they avoid the need to transfer. We analyze how this new type of transportation system relates to existing alternatives and propose an operational concept for it, in which we account for passenger assignment, lead vehicle routes and trailer movements, including empty trailer relocation. We extensively test this concept within a purpose-built simulation environment to evaluate the performance of this kind of system based on real-world data instances. The results highlight the most promising operational policies and characterize favorable system configurations.

A flow-based integer programming approach to design an interurban shared automated vehicle system and assess its financial viability

Shared automated vehicles (SAV) are expected to become part of on-demand transport systems in the future. In an interurban context, SAV systems may potentially contribute to significantly improve the availability and quality of public transport. In this paper, we propose a flow-based integer programming approach to design and assess the financial viability of a SAV interurban system. An integer programming model, based on profit maximization and allowing vehicle pooling, was developed to optimize the fleet size and vehicle movements of a typical working day. This model and its optimal results were the basis for assessing the financial viability of an interurban transport network in the central area of Portugal. Different levels of demand were tested and three different fleet compositions were considered: cars, minibuses, and a mixed fleet of both. The results showed that a mixed fleet of vehicles is the best option for profit maximization. The optimal share of each vehicle type, in this fleet composition, asymptotically converges to approximately 1/3 of cars and 2/3 of minibuses, as demand increases. The introduction of a SAV system in such a region for interurban trips can replace up to 18 private vehicles per SAV vehicle, and should be done with sequential expansions to guarantee a profit at the different stages. The break-even point, for the conditions of the case study, is between 1.5 and 2 passengers per vehicle, for a fleet of cars, and between 4 and 5 passengers per vehicle, for a fleet of minibuses.

Optimizing a vehicle’s route in an on-demand ridesharing system in which users might walk

Within the context of a shared on-demand transport system, we study the problem of selecting the stopping points from which passengers should walk to their exact destinations (or from their exact origins). We focus on the single-vehicle case that must follow a predefined order of requests, posing the mathematical program, showing that it can be solved in polynomial time and proposing a heuristic that runs faster. We compare the optimal algorithm, the heuristic, and the routes that visit the exact request points, and we show that avoiding detours can reduce total costs by almost one fifth and vehicle costs by more than one third. The heuristic yields competitive results. Simulations over the real street network from Manhattan show that the time reduction achieved by the heuristic might be crucial to enable the system to operate in real-time.

ORNInA: A decentralized, auction-based multi-agent coordination in ODT systems

On-Demand Transport (ODT) systems have attracted increasing attention in recent years. Traditional centralized dispatching can achieve optimal solutions, but NP-Hard complexity makes it unsuitable for online and dynamic problems. Centralized and decentralized heuristics can achieve fast, feasible solution at run-time with no guarantee on the quality. Starting from a feasible not optimal solution, we present in this paper a new solution model (ORNInA) consisting of two parallel coordination processes. The first one is a decentralized insertion-heuristic based algorithm to build vehicle schedules in order to solve a particular case of the dynamic Dial-A-Ride-Problem (DARP) as an ODT system, in which vehicles communicate via Vehicle-to-vehicle communication (V2V) and make decentralized decisions. The second coordination scheme is a continuous optimization process namely Pull-demand protocol, based on combinatorial auctions, in order to improve the quality of the global solution achieved by decentralized decision at run-time by exchanging resources between vehicles (k-opt). In its simplest implementation, k is set to 1 so that vehicles can exchange only one resource at a time. We evaluate and analyze the promising results of our contributed techniques on synthetic data for taxis operating in Saint-Étienne city, against a classical decentralized greedy approach and a centralized one that uses a classical mixed-integer linear program (MILP) solver.

Optimizing large on-demand transportation systems through stochastic conic programming

On-demand transportation systems (OTS) are increasingly popular worldwide. Prior literature has studied how to control vehicle fleet in queueing-networks to rebalance excess supply or demand in OTS. This aggregated setting models the stochastic demand process and decompose large-scale networks for which product-form equilibrium distributions exist. However, such an approach is unsatisfactory in terms of computational complexity for its dependence on vehicle numbers. This paper presents a stochastic conic programming approach that obtains the near-optimal vehicle repositioning controls with endogenous demand with mild computational complexity and high fidelity. This global framework covers most existing queueing-network-based OTS models in the literature. Leveraging this approach, we explore day-to-day vehicle repositioning problems for on-demand vehicle operations in New York City. These results support the potential for providing a more accessible and sustainable on-demand mobility service, which is of particular significance as multimodal transport continues to emerge.

Optimizing Large On-Demand Transportation Networks via Approximate Analysis

On-demand transportation systems (OTS) are increasingly popular worldwide. Modeling these systems as closed queueing networks (CQN) or semi-open queueing networks (SOQN) makes optimizations tractable, and controls robust to input data. Prior literature has obtained mean performance measures (e.g., vehicle availability) by decomposing OTS into networks for which product-form equilibrium distributions exist. However, such an approach is unsatisfactory in terms of computational complexity for large networks such as micromobility networks. This paper presents an alternative approach to obtaining mean performance measures with mild computational complexity and high fidelity. Leveraging this approach, we explore day-to-day vehicle redistribution and network pricing problems for OTS operations in New York City. These results support the potential for rebuilding a more accessible and sustainable OTS, which is of particular significance as multimodal transport continues to emerge globally.

Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations

Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). This paper first proposes a new time-discretized multi-commodity network flow model for the VRPPDTW based on the integration of vehicles’ carrying states within space–time transportation networks, so as to allow a joint optimization of passenger-to-vehicle assignment and turn-by-turn routing in congested transportation networks. Our three-dimensional state–space–time network construct is able to comprehensively enumerate possible transportation states at any given time along vehicle space–time paths, and further allows a forward dynamic programming solution algorithm to solve the single vehicle VRPPDTW problem. By utilizing a Lagrangian relaxation approach, the primal multi-vehicle routing problem is decomposed to a sequence of single vehicle routing sub-problems, with Lagrangian multipliers for individual passengers’ requests being updated by sub-gradient-based algorithms. We further discuss a number of search space reduction strategies and test our algorithms, implemented through a specialized program in C++, on medium-scale and large-scale transportation networks, namely the Chicago sketch and Phoenix regional networks.

Management of On-demand Transport Services in Urban Contexts. Barcelona Case Study

Urban mobility transport mostly focuses on collective transport based on largely exploited models such as metro, trains or buses. The basis of current public transport is a fix network of both infrastructure and services, presenting a high lack of flexibility, especially regarding geographical issues. Traditional and innovative on-demand transport services, such as taxi and carsharing respectively, can provide the level of flexibility to the public transport needed to provide both a better service while reducing the exploitation costs. In this context, the study aims to improve the efficiency of on-demand transport systems, mainly taxi and carsharing through the development of analytical models and their application to the city of Barcelona. The optimization of the fleet management and the allocation of resources aim to ensure both the level of service of public transport users and the agency's profitability. The decision variables are the fleet size (number of vehicles) as well as the number and capacity of the depots or stands. Two models are presented, one for the provision of taxi stand service and one for the provision of one way carsharing service. Both models are applied to the demand for taxi services of the city of Barcelona, presenting for each model the optimum number of vehicles and depots, the depots’ capacity, the system unitary costs and the level of service. Although the results show that the performance of both systems is very similar, the taxi service is up to three times more expensive due to the extra cost from the need for having a driver.

Approximation methods for large-scale spatial queueing systems

Different than the conventional queueing systems, in spatial queueing systems (SQS) the service rate for each customer-server pairs differs and the server that intervenes for a specific customer is not known a priori, depending on the availability of servers at the moment a request was made. These features make the SQS computationally expensive (almost intractable for large scale) but at the same time more suitable for real-life problems with high reliability expectations. Emergency response and on-demand transportation systems are two similar systems that can be modeled with the SQS.In this research, we aim to solve facility location problems as SQS with stochastic demand and service time. The stochasticity concerned here is temporal and spatial, that emerges from the uncertainty in the demand and service time. In order to tackle this problem Larson (1974)’s 2n hypercube queueing model (HQM) is extended to 3n HQM. In this model, there are two different possible service types for each server: (i) service for locations in the proximity of a server (area of responsibility) and (ii) service for other locations where the first responsible server is busy during this event. In addition, to decrease the dimension of the problem, which is intractable due to their size, a new 3n aggregate hypercube queueing model (AHQM) is developed that treats group of servers (bins) in a similar manner by considering interactions among bins. An efficient graph partitioning algorithm is proposed to cluster servers in groups with an objective to minimize the interactions among groups. Both exact and approximate approaches are integrated inside two optimization methods (i.e. variable neighborhood search and simulated annealing) to find server locations that improve system performance. Computational experiments showed that both models are applicable to use inside optimization algorithms to find good server locations and to improve system performance measures of SQS.

Multi-objective Iterated Greedy Variable Neighborhood Search Algorithm For Solving a full-load automated guided vehicle routing problem with battery constraints

The Stacker Crane Problem consists in finding a set of minimum-cost roads starts and stops at the same location to satisfy a set of predefined transportation request assuming to have a server with a capacity one. This work deals with an extension of the stacker crane problem that impose distance and time constraints for the roads. We propose specifically an application to this extension on specific set of on-demand transportation system. We propose also an approach based on Variable Neighborhood Search and iterated greedy metaheuristics to deal with the multi-objective version of this problem. One important feature of our specific algorithm is the use of the distruction/contruction method as a shake function for the Variable Neighborhood Search algorithm. This feature combined with the multi-neighborhood Search led us to a preliminary promising results.

Optimization of occupancy rate in dial-a-ride problems via linear fractional column generation

In this paper, we consider a dial-a-ride problem where the objective is to maximize the passenger occupancy rate. The problem arises from an on-demand transportation system developed in a rural zone in France, where the objective of encouraging people meeting is pursued. We address the solution of the problem with a column generation approach, applied to a set partitioning formulation where the objective function is fractional. Based on the literature on linear fractional programming, two methods are developed to deal with this fractional objective. Experiments permit to compare these two approaches and to evaluate the impact of the new objective compared to a standard min-cost or min-time optimization.

Vehicle routing problems with alternative paths: An application to on-demand transportation

The class of vehicle routing problems involves the optimization of freight or passenger transportation activities. These problems are generally treated via the representation of the road network as a weighted complete graph. Each arc of the graph represents the shortest route for a possible origin–destination connection. Several attributes can be defined for one arc (travel time, travel cost, etc.), but the shortest route modeled by this arc is computed according to a single criterion, generally travel time. Consequently, some alternative routes proposing a different compromise between the attributes of the arcs are discarded from the solution space. We propose to consider these alternative routes and to evaluate their impact on solution algorithms and solution values through a multigraph representation of the road network. We point out the difficulties brought by this representation for general vehicle routing problems, which drives us to introduce the so-called fixed sequence arc selection problem (FSASP). We propose a dynamic programming solution method for this problem. In the context of an on-demand transportation (ODT) problem, we then propose a simple insertion algorithm based on iterative FSASP solving and a branch-and-price exact method. Computational experiments on modified instances from the literature and on realistic data issued from an ODT system in the French Doubs Central area underline the cost savings brought by the proposed methods using the multigraph model.

Route optimisation using evolutionary approaches for on-demand pickup problem

The development of information technologies realises an on-demand transport (pick-up) system. In this paper, we simulate transport situations for the system based on multi-agent model to find efficient strategies. We examine four types of driver agents; random agent, greedy agent, Q-learning agent, and Genetic agent. Random agent and Greedy agents select the next pick-up points from its surround without learning and optimisation. In contrast, Q-learning agent estimates the expectation value of pick-up quantity by Q-learning, and Genetic agent optimises its travel routes by Genetic algorithm. Finally, we report our experimental results to evaluate the effect of the four strategies.