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Integrating parcel deliveries into a ride-pooling service—An agent-based simulation study
Abstract
This paper examines the integration of freight delivery into the passenger transport of an on-demand ride-pooling service. The goal of this research is to use existing passenger trips for logistics services and thus reduce additional vehicle kilometers for freight delivery and the total number of vehicles on the road network. This is achieved by merging the need for two separate fleets into a single one by combining the services. This research provides an extensive literature review and discusses policy measures supporting such a service. To evaluate the potential of such a mobility-on-demand service, this paper uses an agent-based simulation framework and integrates three heuristic parcel assignment strategies into a ride-pooling fleet control algorithm. Two integration scenarios (moderate and full) are set up. While in both scenarios passengers and parcels share rides in one vehicle, in the moderate scenario no stops for parcel pick-up and delivery are allowed during a passenger ride to decrease customer inconvenience. Using real-world demand data for a case study of Munich, Germany, the two integration scenarios together with the three assignment strategies are compared to the status quo, which uses two separate vehicle fleets for passenger and logistics transport. The results indicate that the integration of logistics services into a ride-pooling service is possible and can exploit unused system capacities without deteriorating passenger transportation. Depending on the assignment strategies nearly all parcels can be served until a parcel to passenger demand ratio of 1:10 while the overall fleet kilometers can be decreased compared to the status quo.
... Free-floating systems are mainly found in the private or semi-private transport sector and can be divided into crowd-sourced solutions [36][37][38][39] and demand responsive transportation services [40][41][42][43][44][45]. In the case of crowdsourced solutions, a distinction can be made between integration approaches that consider a hired fleet of vehicles [36][37][38][39] and occasional drivers [36,38,39], recruited through an online platform. ...
... For demand-responsive transportation services, one can distinguish between ride-haling (i.e., one request/customer per trip) [38,40,41] and ride-pooling (i.e. multiple requests/customers share a vehicle on their trip) [42,[44][45][46][47] can be made. Furthermore, the research papers consider immediate [36-41, 43, 44] and scheduled [42,45] logistics services and can be divided into static and dynamic models. ...
... multiple requests/customers share a vehicle on their trip) [42,[44][45][46][47] can be made. Furthermore, the research papers consider immediate [36-41, 43, 44] and scheduled [42,45] logistics services and can be divided into static and dynamic models. ...
Ride Parcel Pooling (RPP) is the integrated transportation of passengers and parcels. It builds on what's known as on-demand ride-pooling, which dynamically assigns customer trips to fleet vehicles in real time, sharing their rides, and thus aiming to save on driving distances and fleet size compared to on-demand ride-hailing services. In the case of RPP, a additional parcel demand is introduced that is less sensitive to longer travel and waiting times and is less negatively affected by changing vehicles. In this case, parcels are served with a lower priority than passengers and simply ride along with appropriate passenger trips. This makes it possible to achieve a more efficient use of vehicle capacity and further reduce the total distance traveled for passenger and logistics mobility. This research is divided into a conceptual and scenario definition part to define the proposed service and develop possible operational scenarios, a theoretical simulation-based approach to quantify the potential of the proposed RPP service, and a practical field test to investigate its real-world applicability. Agent-based RPP simulations show that the integration of logistics services into a ride-pooling service is possible and can exploit unused system capacity without degrading passenger service. Depending on the assignment strategies, parcels can be served up to a parcel-to-passenger demand ratio of 1:10, while total fleet kilometers can be reduced compared to the status quo, as the additional mileage for logistics service can be fully integrated. In the base scenario of the simulation, approximately 50,000 passengers and 5,000 parcels are transported by a fleet of 600 cars or 1,200 rickshaws. The RPP field test included a mobile phone web app and five bicycle rickshaws offering the RPP service in the Maxvorstadt district in Munich, Germany. Each rickshaw had two passenger seats and additional space for parcels. The service was available daily between 11:00 and 19:00 for one week and was completely free for users. The field test showed that the RPP service is ready to operate today and provided interesting insights into the real-world operational parameters for such a service.
... The vehicle must arrive at the pickup site within a certain time range, and the duration of the customer's trip within the vehicle is predetermined. In Fehn et al. [20] the integration of goods delivery into the passenger transport of an on-demand ride-pooling service is investigated. The objective of the study is to use pre-existing passenger trips to provide logistical services, hence minimising the need for extra kilometres travelled for goods transportation and reducing the overall number of cars on the road network. ...
... A very effective method is proposed and confirmed by the advanced optimisation of periodic transportation subproblems. Fehn et al. [20] discussed the integration of passenger and freight transport in a parcel delivery service called Ride-Parcel-Pooling (RPP) inside an on-demand mobility ride-pooling system. The vehicle fleet operator integrates package delivery into the passenger routes of the 'mobility on demand' (MoD) service. ...
... Sharing infrastructure (Pimentel & Alvelos, 2018;Risimati et al., 2021;Cebeci et al., 2023;Tapia et al., 2023;Cleophas et al., 2019;Fehn et al., 2023;Fessler et al., 2022) P4 First-last mile transport (Arvidsson et al., 2016;Bruzzone et al., 2021;Lan et al., 2018 Organizer of transport (Cieplinska, 2018;Cieplińska, 2019) Prisma procedure consists of 4 stages in this case, are as follows (Akhigbe et al., 2017): ...
... Fehn et al. (Fehn et al., 2023) work demonstrates the sharing of vehicle spaces in on-demand ride-pooling services. Since the research provides an extensive literature review and integration scenarios, it omits the already existing services in this form in the US. ...
... The taxi route is planned according to the passenger request, and a parcel request is then inserted into the route. Similar to taxis, Fehn et al. (2023) integrated parcel deliveries into mobility on-demand services (MoD), named ride parcel pooling, using an agent-based simulation framework with three heuristics to assign parcels to the carpooling fleet. The case study shows that MoD service does not deteriorate with the integration of freight delivery until the ratio of parcels to passengers is greater than 1/10. ...
... Multi-purpose autonomous vehicles with flexible conversion of passenger or logistics space (Fehn et al., 2023) and modular autonomous buses with naturally independent compartments (Lin et al., 2023) are more suitable for RBITS-IC. Their acquisition and operation may involve public-private partnership cooperation in the future. ...
A rural bus integrating passenger and freight transport is a new effective public transit mode to realise village interconnection and solve the first–last mile rural logistics service. Mixed services reduce logistics costs and generate additional income for transit operators. However, the mutual interpenetration of logistics and passenger services in a single bus trip may lead to a decline in the service quality of both passengers and goods. With this in mind, we designed a government subsidy incentive contract and a logistics alliance payment incentive contract respectively from the perspective of participants based on the principal-agent theory. In addition, a bi-level programming model consisting of two principals (government and logistics alliance) and one agent (transit operator) was proposed to incentivise bus operators and improve passenger and freight service quality. It scientifically realises the coordination of interests between the principals and agents. A Chinese case study was conducted to examine the proposed models and methods, and a passenger-based incentive subsidy programme from the government was proposed to replace the mileage-based one. With both incentive policies (government and logistics alliance), passenger travel time can be reduced by 4.2 min, and logistics transportation time can be shortened by 30 min. In addition, the total annual government budget can be reduced by 17.2% (¥450,000, $64,000) and annual fleet mileage savings by approximately 9.4% (62,000 km) compared with the mileage-based incentive programme in the case study. A sensitivity analysis is conducted to explore the impact on the collaborative system. This innovative concept, combined with incentive schemes, is a good reference for public administration to avoid the dilemma of having passenger and freight transport in low-demand areas.
... Furthermore, when considering the mode choices preferred by occasional couriers in their CS deliveries, the concept of integrating passenger and freight transportation (Marcucci et al., 2017;Pimentel and Alvelos, 2018;Bruzzone et al., 2021;Fehn et al., 2023) may find a pathway for real-world implementation. The cost-related aspects estimated within this study, particularly concerning the coverage of CS travel by occasional couriers and the provision of remuneration for their services, lay a solid foundation for developing initiatives aimed at encouraging people to adopt more sustainable modes of transportation. ...
This paper presents the results of a behavioral study on consumers' willingness-to-pay the extra for e-grocery deliveries based on crowd-shipping. The proposed methodology was tested for Ukraine, i.e., a developing country where the crowd-shipping services are under development conditions. The choice model was enhanced with two latent variables to account for the behavior complexity of the consumers who have not faced crowd-shipping services in the past. These variables were extracted through factor analysis to encompass pro-crowd-shipping and pro-commercial carrier attributes. The willingness-to-pay for e-grocery deliveries based on crowd-shipping and commercial carriers was estimated using hybrid choice modeling results. The findings indicate more than two times higher willingness-to-pay for saving delivery time for crowd-shipping compared to commercial carrier services. Discrete choice analysis of consumer-related attributes identified males with cons-crowd-shipping attitudes. Age-wise, consumers younger than 30 years exhibited pro-crowd-shipping behavior. The direct and cross elasticities have been estimated to evaluate the impacts of variation in service-specific attributes on the consumer's behavior within e-groceries scope.
... Inspired by Fehn et al. (2023), we investigate an operational strategy for vehicle-based mobile crowd-sensing that integrates sensing tasks with ride-hailing requests, incentivizing drivers to undertake tasks while maintaining service levels for regular riders. In this business model, a data user collaborates with a ride-hailing platform by releasing a third-party app for sensing tasks and commissioning these tasks to a pool of available drivers. ...
This paper evaluates the benefit of integrating vehicle-based mobile crowd-sensing tasks into the ride-hailing system through the collaboration between the data user and the ride-hailing platform. In such a system, the ride-hailing platform commissions high-valued sensing tasks to idle drivers who can undertake either ride-hailing or sensing requests. Considering the different service requirements and time windows between sensing and ride-hailing requests, we design a staggered operation strategy for ride-hailing order matching and the sensing task assignment. The auction-based mechanisms are employed to minimize costs while incentivizing driver participation in mobile sensing. To address the budget deficit problem of the primal VCG-based task assignment mechanism, we refine the driver selection approach and tailor the payment rule by imposing additional budget constraints. We demonstrate the benefits of our proposed mechanism through a series of numerical experiments using the NYC Taxi data. Experimental results reveal the potential of the mechanism for achieving high completion rates of sensing tasks at low social costs without degrading ride-hailing services. Furthermore, drivers who participate in both mobile sensing tasks and ride-hailing requests may gain higher income, but this advantage may diminish with an increasing number of such drivers and higher demand for ride-hailing services.
... Therefore, further research could combine the usage of shared-use connected automated vehicles for people trips and PDO delivery to improve outcomes, such as reducing carbon emissions and improving vehicle utilization. Fehn et al. (2023) present a promising approach for integrating packages and people in a logistics service, where they use heuristic insertion algorithms in an agent-based simulation. Insert u * to (i, j) * in r * , update r * . ...
This paper presents a set-partitioning formulation and a novel decomposition heuristic (D-H) solution algorithm to solve large-scale instances of the urban crowdsourced shared-trip delivery (CSD) problem. The CSD problem involves dedicated vehicles (DVs) and shared personal vehicles (SPVs) fulfilling delivery orders, wherein the SPVs have their own trip origins and destinations. The D-H begins by assigning as many package delivery orders (PDOs) to SPVs as possible, where the D-H enumerates the set of routes each SPV can feasibly traverse and then solves a PDO-SPV-route assignment problem. For PDO-DV assignment and DV routing, the D-H solves a multi-vehicle routing problem with time-window, tour duration, and capacity constraints using an insertion heuristic. Finally, the D-H seeks potential solution improvements by switching PDOs between SPV and DV routes through a simulated annealing (SA)-inspired procedure. The D-H outperforms a commercial solver in terms of computational efficiency while obtaining near-optimal solutions for small problem instances. The SA-inspired switching procedure out-performs a large neighborhood search algorithm regarding run time, and the two are comparable regarding solution quality. Finally, the paper uses the D-H to analyze the impact of several relevant factors on city-scale CSD system performance, namely the number of participating SPVs and the maximum willingness to detour of SPVs. Consistent with the existing literature, we find that CSD can substantially reduce delivery costs. However, we find that CSD can increase vehicle miles traveled. Our findings provide meaningful insights for logistics practitioners, while the algorithms illustrate promise for large real-world systems.
... The studies, however, mostly focus on engineering challenges and methodological developments, from the management / operations research perspective. In order perform a successful transition to actionable change, it is required to also take into account the policy perspectives, since policies derived from research findings shape governmental decisions, organizational strategies, and societal frameworks; see the following policy-related papers with focus on air mobility-based parcel delivery: Fehn et al. (2023) ; Janjevic and Winkenbach (2020) ; Kim and Wang (2022) ;Straubinger et al. (2023) ; Zhu et al. (2023) . Accordingly, CDTD-related policies should preferably be discussed and set before industry players, especially technologydriven startups, create precedents. ...
The application of drone technology promises to revolutionize the transportation industry. Particularly, the combination of drones with ground vehicles has tremendous advantages for delivery applications, including an increased delivery speed and reduced operating costs, while keeping drones lightweight and small. Accordingly, the number of research studies targeting the Coordinated Delivery of Trucks and Drones (CDTD) has increased significantly in the past decade. Most of these existing studies, however, have put a strong emphasis on the optimization aspects, usually by solving combinatorial problems induced by the delivery coordination and the goal to minimize a specific objective function. Here, we contribute to the extant body of literature by providing a comprehensive review and discussion of policy-related challenges for a successful CDTD implementation. Given that various industry stakeholders, e.g., Amazon, Uber, and SF express, are already in the process of pushing the envelope for CDTD operations, we believe that our contribution is timely and complementary, helping policy makers to make informed decision regarding the support and regulation of this new technology.
... Como indicadores financieros, se propone el uso del KPI Financiero (Key Performance Indicator), basado en datos financieros de transportistas [42]. Se examinaron los ingresos (revenue) generados por el servicio de distribución urbana, considerando la entrega de paquetes [43]. Se trabajó las ganancias como un índice de rentabilidad [44] y se usaron métricas clave como la Relación Beneficio-Costo (BCR) y el Valor Presente Neto (VPN) [32], evaluando la viabilidad económica de proyectos logísticos urbanos. ...
Este artículo presenta una revisión sistemática de la literatura sobre Distribución Urbana de Mercancías (DUM) en logística de última milla, utilizando la metodología PRISMA y un análisis bibliométrico basado en análisis estadísticos de calidad y cantidad. Se realizaron búsquedas en bases de datos como Scopus y Web of Science, identificando tendencias, coautorías y patrones a lo largo del tiempo. Se destaca un crecimiento anual en las publicaciones, junto con palabras clave recurrentes, autores influyentes y revistas relevantes en el ámbito de las entregas urbanas. Se propone una taxonomía de clasificación con diez diferentes tipos de indicadores de desempeño en la DUM con tres métodos diferentes de evaluación y su campo de aplicación. Este análisis cuantitativo y cualitativo proporciona una base sólida para futuras investigaciones en logística urbana y distribución de mercancías
... Como indicadores financieros,[42] proponen el KPI Financiero (Key Performance Indicator), basado en datos financieros de transportistas.[43] examinaron los Ingresos (Revenue) generados por el servicio de distribución urbana, considerando la entrega de paquetes.[44] ...
... The global logistics delivery market is expected to reach $622.69 billion in revenue by 2029 (Research, 2023). This growth is driven by the abovementioned factors and the increasing demand for logistics delivery services from businesses and consumers (Fehn et al., 2023). The logistics delivery market is highly competitive, and the key players are constantly innovating to stay ahead of the competition (Kulkarni et al., 2022). ...
This research investigates the potential advantages of using artificial intelligence (AI) to drive ensemble machine learning (ML) for enhancing cost strategies and maximizing profits. This study aims to explore the ability of AI-powered ensemble ML to optimize cost strategies by simulating business threshold cost data to determine optimal mitigation strategies. The dataset comprises 6561 potential tuples, and three ensemble ML methods are employed as ML algorithms to identify patterns and relationships in the cost data for strategic decisions. The originality of this project lies in its demonstration of the capacity of simulated data to enhance cost-saving strategies for businesses. This research contributes to the existing literature on AI and ML applications in business by revealing the potential of ML applications for business owners and personnel involved in production and marketing. The findings of this research have significant implications for a wide range of industries, including transportation, logistics, and retail.
... Initially, the author introduced an MGOA with the fusion of the GOA algorithm. Here, the pooling centers are located with the support of the multi-objective function [38,39]. The pooling center is determined using the objective function. ...
Due to the emergence of technology, electric motors (EMs), an essential part of electric vehicles (which basically act as engines), have become a pivotal component in modern industries. Monitoring the spare parts of EMs is critical for stabilizing and managing industrial parts. Generally, the engine or motor parts are delivered to factories using packing boxes (PBs). This is mainly achieved via a pooling center that manages the operation and transportation costs. Nevertheless, this process has some drawbacks, such as a high power train, bad press, and greater energy and time consumption, resulting in performance degradation. Suppliers generally take the parts from one place and deliver them to the other, which leads to more operation and transportation costs. Instead, it requires pooling centers to act as hubs, at which every supplier collects the material. This can mitigate the cost level. Moreover, choosing the placement of pooling centers is quite a challenging task. Different methods have been implemented; however, optimal results are still required to achieve better objectives. This paper introduces a novel concept for pooling management and transport optimization of engine parts to overcome the issues in traditional solution methodologies. The primary intention of this model is to deduce the total cost of the system operation and construction. Programming techniques for transporting the PBs, as well as for locating the pooling center, are determined with the aid of an objective function as a cost function. The location of the pooling center’s cost is optimized, and a Modified Gannet Optimization Algorithm (MGOA) is proposed. Using this method, the proposed model is validated over various matrices, and the results demonstrate its better efficiency rate.
... Various solutions exist to solve the VRP; some are dynamic statics, dynamic stochastic, and dynamic distributed decision-based techniques (Ibarra-Rojas, Hernandez, and Ozuna 2018). (2020) Prioritizing High-Priority Deliveries Fehn et al. (2023) According to Koç, Laporte, and Tükenmez (2020), the VRP problem can be divided into subproblems or variants, as shown in Table 1. Table 2 lists the classification of VRP into four main categories and its subcategories; the first category is Many-to-Many problems (M2MP), the second category is One-to-Many-to-One pickup and delivery problems (1-M-1-PDP), the Third category is 1-M-1-PDP with single demands (1-M-1-PDP-SD), and the fourth category is One-to-one problems (1-1-P). ...
COVID-19 is responsible for medical emergencies, and uncertainty in healthcare supply chain (HSC). The HSC is struggling against the massive disruption due to the pandemic; thus, this alarming trend reinforces the need for a resilient HSC and a unique dynamic, responsive plan. The pandemic has strained hospitals and quarantine centres, necessitating crisis management for resource allocation. Addressing HSC issues, we've meticulously analysed vehicle routing problems (VRP) and devised solutions. The proposed methodology is exploring the dynamic programming (DP) for resilient HSC. The study offers a single-vehicle routing strategy with simultaneous loading and unloading of items between the depot and N nodes (hospitals and quarantine centres) to alleviate the vulnerability of commodities and recovery of items. The implication of this work is to provide dynamics of special purpose vehicle (SPV) with two storage facilities, one dedicated to fresh healthcare products (depot to nodes) and the second dedicated to old/recyclable/disposable healthcare products (nodes to depot). Finally, utilising the DP and particle swarm optimisation introduces an innovative strategy that compares efficient routing and allocation decisions for resilient HSC. It offers practitioners guidance on optimal operational sequences, optimal cost, and computational times for SPV, enhancing HSC response capabilities during crises.
... A Bayesian optimization approach is employed to find a dynamic priority policy that determines the time-dependent size of the priority fleet. A similar setting is considered in [66], where the authors developed an agent-based simulation approach to investigate the integrated transportation of passengers and parcels with the same fleet of on-demand vehicles assuming that passengers has priority over parcels. It illustrates that the proposed business model has no negative impacts on the ride-sourcing services and can serve nearly all parcels when the parcel-to-passenger demand ratio is not too large. ...
This paper investigates the operational strategies for an integrated platform that provides both ride-sourcing services and intracity parcel delivery services over a transportation network utilizing the idle time of ride-sourcing drivers. Specifically, the integrated platform simultaneously offers on-demand ride-sourcing services for passengers and multiple modes of parcel delivery services for customers, including: (1) on-demand delivery, where drivers immediately pick up and deliver parcels upon receiving a delivery request; and (2) flexible delivery, where drivers can pick up (or drop off) parcels only when they are idle and waiting for the next ride-sourcing request. A continuous-time Markov Chain (CTMC) model is proposed to characterize the status change of drivers under joint movement of passengers and parcels over the transportation network with limited vehicle capacity, where the service quality of ride-sourcing services, on-demand delivery services, and flexible delivery services are rigorously quantified. Building on the CTMC model, incentives for ride-sourcing passengers, delivery customers, drivers, and the platform are captured through an economic equilibrium model, and the optimal operational decisions of the platform are derived by solving a non-convex profit-maximizing problem. We prove the well-posedness of the model and develop a tailored algorithm to compute the optimal decisions of the platform at an accelerated speed. Furthermore, we validate the proposed model in a comprehensive case study for San Francisco, demonstrating that joint management of ride-sourcing services and intracity package delivery services can lead to a Pareto improvement that benefits all stakeholders in the integrated ride-sourcing and parcel delivery market.
... Firstly, the impact of new MoD service concepts will be explored. For example, the underutilized vehicles in times of low demand can be used for parcel transport Fehn et al. [2022]. Simulation can be used to evaluate possible benefits and whether this integration is a business case for operators. ...
The market share of mobility on-demand (MoD) services strongly increased in recent years and is expected to rise even higher once vehicle automation is fully available. These services might reduce space consumption in cities as fewer parking spaces are required if private vehicle trips are replaced. If rides are shared additionally, occupancy related traffic efficiency is increased. Simulations help to identify the actual impact of MoD on a traffic system, evaluate new control algorithms for improved service efficiency and develop guidelines for regulatory measures. This paper presents the open-source agent-based simulation framework FleetPy. FleetPy (written in the programming language "Python") is explicitly developed to model MoD services in a high level of detail. It specially focuses on the modeling of interactions of users with operators while its flexibility allows the integration and embedding of multiple operators in the overall transportation system. Its modular structure ensures the transferabillity of previously developed elements and the selection of an appropriate level of modeling detail. This paper compares existing simulation frameworks for MoD services and highlights exclusive features of FleetPy. The upper level simulation flows are presented, followed by required input data for the simulation and the output data FleetPy produces. Additionally, the modules within FleetPy and high-level descriptions of current implementations are provided. Finally, an example showcase for Manhattan, NYC provides insights into the impacts of different modules for simulation flow, fleet optimization, traveler behavior and network representation.
The current society is becoming increasingly interconnected and hyper-connected. Communication networks are advancing, as well as logistics networks, or even networks for the transportation and distribution of natural resources. One of the key benefits of the evolution of these networks is to bring consumers closer to the source of a resource or service. However, this is not a straightforward task, particularly since networks near final users are usually shaped by heterogeneous nodes, sometimes even in very dense scenarios, which may demand or offer a resource at any given moment. In this paper, we present DEN2NE, a novel algorithm designed for the automatic distribution and reallocation of resources in distributed environments. The algorithm has been implemented with six different criteria in order to adapt it to the specific use case under consideration. The results obtained from DEN2DE are promising, owing to its adaptability and its average execution time, which follows a linear distribution in relation to the topology size.
Taxi crowdsourcing has gained great interest from the logistics industry and academe due to its significant economic and environmental impact. However, existing approaches have several limitations and focus solely on single objective optimization problem. In this paper, we propose a three-stage framework, namely MOOP4PD to improve the existing approaches. Firstly, we propose a DesCloser* pruning algorithm with no limitation on taxi capacity and use A* algorithm to further optimize the delivery routes. Then, a novel multi-objective pruning algorithm, named MDesCloser*, is presented to find the non-dominated set, which contains waiting time window MaxWT and taxi capacity MaxC constraints. Finally, we develop a constraint solving approach to obtain the ideal solution (i.e., MaxWT equals 11 and MaxC equals 6). We evaluate the performance using the data set generated by Brinkhoff road network generator in the city of Luoyang, China. Results show that our approach improve the objectives of success rate, average number of participating taxis, average delivery distance and average delivery time. Especially, MDesCloser* have best performance on the success rate with more than 0.88 and minimize the total waiting time of all packages to 14916.6 time slices if failure in delivering and maximize the average transshipping rate of interchange stations.
Serving both passenger and freight demand with the same vehicle fleet is an ambition that led to the development of several innovative vehicle concepts [10, 26]. This study proposes a simulation-based methodology to investigate the execution of freight tours with an On-Demand fleet of autonomous, modular vehicles while giving priority to passenger transport. Based on assumptions regarding the operational scheme, tour pricing and delivery time windows, the software Multi-Agent Transport Simulation (MATSim) is extended. The developed methodology is then applied to a Berlin-wide freight delivery scenario. The results show that when using a relatively large vehicle fleet, passenger waiting time statistics barely change. However, it has to be noted that the study should be repeated with different (smaller) fleet sizes. A rough cost analysis for the freight operator suggests that there is a large saving potential when using autonomous On-Demand vehicles instead of an owned fleet. Because of the uncertainty of price composition, further studies to quantify this saving potential have to be made.
Autonomous vehicle (AV) integration poses a significant challenge for intelligent transportation systems (ITSs). The ability to automatically coordinate complex AV operations at scale is crucial for advancing the quality of core transportation services, such as ride-sharing and parcel delivery. However, existing studies have only considered either of these two services independently from the other, disregarding the potential benefits of their combined optimization. To address this open problem, we design an autonomous vehicle intelligent system (AVIS) providing joint ride-sharing and parcel delivery services under realistic ride and route constraints. We formulate the joint optimization problem through the scope of mixed-integer linear programming and solve it using the Lagrangian dual decomposition method to ensure scalability. We conduct extensive case studies to evaluate the performance of the proposed AVIS and its constituting components. Our experimental results demonstrate that AVIS can effectively provide both ride-sharing and parcel delivery services while satisfying service requests in transportation networks of various scales. In addition, the distributed method is shown to generate near-optimal solutions in reduced computation time.
Time-sensitive parcel deliveries—shipments requested for delivery in a day or less—are an increasingly important aspect of urban logistics. It is challenging to deal with these deliveries from a carrier perspective. These require additional planning constraints, preventing the efficient consolidation of deliveries that is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities, such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo hitching. While there are various system designs, it is crucial that such a solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of mobility-on-demand (MOD) services that perform same-day parcel deliveries. To test the MOD-based solutions, we utilize a high-resolution agent- and activity-based simulation platform of passenger and freight flows. E-commerce demand carrier data collected in Singapore are used to characterize simulated parcel delivery demand. We explore operational scenarios that aim to minimize the adverse effects of fulfilling deliveries with MOD service vehicles on passenger flows. Adverse effects are measured in fulfillment, wait, and travel times. A case study on Singapore indicates that the MOD services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle kilometers travelled without compromising the quality of MOD for passenger travel. Insights into the operational performance of the cargo-hitching service are also provided.
Ride- Parcel- Pooling expands conventional ride-pooling on-demand mobility services. It does not only offer passengers shared rides but also integrates parcels into the assignment problem to increase vehicle utilization during times of low passenger demand. This article examines the potential behind the combination of on-demand mobility and city logistics using a case study for the city of Munich, Germany. We examined and evaluated the effects of Ride-Parcel-Pooling in three different scenarios. The first scenario replicates the status quo (two separate services for passenger and parcel delivery), while scenarios two and three examine different forms of combining on-demand mobility and city logistics. This study shows that about 80 % of the distance traveled to provide logistics services could be saved. Within the case study, this work also provides insights on the environmental impacts of Ride-Parcel-Pooling by analyzing the most prominent global and local emission parameters of road traffic. Results show a reduction of overall CO 2 emissions as well as of the investigated local air pollutants NOx, PM 10 , CO, and SO 2 .
The shared autonomous vehicle (SAV) is a new concept that meets the upcoming trends of autonomous driving and changing demands in urban transportation. SAVs can carry passengers and parcels simultaneously, making use of dedicated passenger and parcel modules on board. A fleet of SAVs could partly take over private transport, taxi, and last-mile delivery services. A reduced fleet size compared to conventional transportation modes would lead to less traffic congestion in urban centres. This paper presents a method to estimate the optimal capacity for the passenger and parcel compartments of SAVs. The problem is presented as a vehicle routing problem and is named variable capacity share-a-ride-problem (VCSARP). The model has a MILP formulation and is solved using a commercial solver. It seeks to create the optimal routing schedule between a randomly generated set of pick-up and drop-off requests of passengers and parcels. The objective function aims to minimize the total energy costs of each schedule, which is a trade-off between travelled distance and vehicle capacity. Different scenarios are composed by altering parameters, representing travel demand at different times of the day. The model results show the optimized cost of each simulation along with associated routes and vehicle capacities.
Ride hailing (RH) services have become a common mode of transportation in the last decade. Usually, statistical tools are used to improve their performance, whereby the tools typically divide the whole operational area into multiple regions. These tools usually assume that the regions are independent of each other even though vehicles from one region can be used to serve neighboring regions, thereby a method is required that consistently relates vehicle demand and supply between geographically neighboring regions or to the whole operating area. This hinders tapping into the full potential of region-based performance improvement techniques like the repositioning of idle vehicles. Therefore, we develop an innovative reachability function-based method that coherently builds a relation among all regions in the form of a spatial density of the measured quantity. We use it to calculate the differences of vehicle supply and demand for the whole operational area in the form of an imbalance density. Based on this, we derive a novel repositioning formulation that significantly reduces both the overall vehicle imbalances and the total distance of repositioning trips, and thus improves the long-term RH performance. We test the approach in an agent-based simulation for an RH scenario with automated vehicles, using open-source New York City taxi data as demand. The approach shows a remarkable improvement over the state of the art repositioning strategies that balance the fleet over the individual regions. Furthermore, we introduce kernel-based key performance indicators (KPIs) that can be calculated at the time of making repositioning decisions. We also show the correlation of the KPIs with long-term performance gains. We expect that these KPIs can benefit future statistical (machine learning) approaches for repositioning.
With ongoing developments in digitalization and advances in the field of autonomous driving, on-demand ride pooling is a mobility service with the potential to disrupt the urban mobility market. Nevertheless, to apply this kind of service successfully efficient algorithms have to be implemented for effective fleet management to exploit the benefits associated with this mobility service. Especially real time computation of finding beneficial assignments is a problem not solved for large problem sizes until today. In this study, we show the importance of using advanced algorithms by comparing a fast, but simple insertion heuristic algorithm with a state-of-the-art multi-step matching algorithm. We test the algorithms in various scenarios based on private vehicle trip OD-data for Munich, Germany. Results indicate that in the tested scenarios by using the multi-step algorithm up to 8 additional requests could be served while also 10 additional driven distance could be saved. However, computational time for finding optimal assignments in the advanced algorithm exceeds real time rather fast as problem size increases. Therefore, several aspects to reduce the computational time by decreasing redundant checks of the advanced multi step algorithm are introduced. Finally, a refined vehicle selection heuristic based on three rules is presented to furthermore reduce the computational effort. In the tested scenarios this heuristic can speed up the most cost intensive algorithm step by a factor of over 8, while keeping the number of served requests almost constant and maintaining around 70 of the driven distance saved in the system. Considering all algorithm steps, an overall speed up of 2.5 could be achieved.
On-demand ride-pooling (ODRP) services have the potential to improve traffic conditions in cities and at the same time offer user-centric mobility services. Recently, an analytical model, which investigates the influence of service quality parameters, such as detour, maximum waiting time, and boarding time, on the fraction of trips which could potentially be shared (a quantity called shareability), has been presented. The aim of this study is to test this model with a simulation framework, which models an ODRP service in different levels of details. The results show that by increasing the modeling complexity, where we consider network topology, trip distribution patterns, optimization objectives and changing velocity, the theoretical value of shareability and the actual experienced shared rides are decreased. It is observed that the shareability predicted by the mathematical model could be confirmed by a certain simulation setup with the objective to maximize shared rides. Nevertheless, changing the optimization objective to optimizing the total kilometers driven has the highest impact on shareability, decreasing it by up to 50%. By using a fitting procedure within this simulation setup, we can still exploit the analytical model to predict the influence of service quality parameters. This study can be useful for other researchers who plan to model ride pooling systems and for operators who want to have an estimation of the level of shared rides they can achieve in an operating area.
Freight transport based on public transport is an indication of integration of passenger and freight transportation. Relative problem and practical applications of freight transport based on public transit were studied and four operating modes of freight transport by rail were summarized in this paper. Additionally, parcel transport capacity of a single subway line in different modes and parcels demand in main urban areas were estimated. For freight transport through subway network, the appropriate operating modes of freight transport under three different demand scenarios were analyzed as well, and corresponding environmental benefits under these three modes were evaluated separately. The result shows that using the subway to transport goods can efficiently reduce carbon emissions by 20–50%. Furthermore, when the demand is low and dispersed, the trolley and roller container can be used to load the goods. When the demand is relatively high or when dispersed demand can be merged, the single freight train or mixed train with passengers and freight in different carriages need to be used.
Many national and international bodies, such as the European Commission, encourage the use of environment-friendly transport modes. Local and national authorities take more and more measures, for instance road pricing, loading/unloading spaces and low-emission zones, to prevent negative transport-related externalities in urban areas. Hence, transport and logistics operators consider alternative ways to deliver goods in urban areas by using electric vehicles, cargo bikes, inland vessels and rail transport. Which of these alternative modes is appropriate for which transport flow depends on multiple factors, including the available transport infrastructure, the goods volume, the measures taken by the authorities and the presence of congestion. This paper focuses on urban freight transport by tram and the conditions for a successful implementation. A successful implementation is defined as an implementation that is viable, i.e. the difference between the change of the costs and the change of the benefits exceeds a certain threshold value. The viability is studied from a business-economic and a socio-economic perspective for a dedicated freight tram, a freight wagon behind a passenger tram and the transport of parcels by a passenger tram. A viability model is developed, based on a social cost-benefit analysis. The working of this model is illustrated by applying it to the city of Antwerp. The main findings show that the use of a freight wagon attached to a passenger tram provides more potential than a dedicated freight tram. A courier taking the tram to deliver some parcels can be viable as well. For all three types of tram transport, the socio-economic benefits exceed the business-economic ones. Critical factors affecting the viability include the transported volume, the efficiency of the current road transport, the timing of the transport, the need for post-haulage and the operational costs of both road and rail.
The rise of research into shared mobility systems reflects emerging challenges, such as rising urbanization rates, traffic congestion, oil prices and environmental concerns. The operations research community has turned towards more sharable and sustainable systems of transportation. Although shared mobility comes with many benefits, it has some challenges that are restricting its widespread adoption. More research is thus needed towards developing new shared mobility systems so that a better use of the available transportation assets can be obtained.This thesis aims at developing efficient models and optimization approaches for synchronizing people and freight flows in an urban environment. As such, the following research questions are addressed throughout the thesis:*Q1: What are the variants of shared mobility systems and how to optimize them?*Q2: How can people trips be synchronized and what gains can this synchronization yields?*Q3: How can people and freight flows be combined and what are the intended benefits?*Q4: What impacts uncertainty can have on planning and operating shared mobility systems?First, we review different variants of the shared mobility problem where either (i) travelers share their rides, or (ii) the transportation of passengers and freight is combined. We then classify these variants according to their models, solution approaches and application context and We provide a comprehensive overview of the recently published papers and case studies. Based on this review, we identify two shared mobility problems, which we study further in this thesis.Second, we study a ridesharing problem where individually-owned and on-demand autonomous vehicles (AVs) are used for transporting passengers and a set of meeting points is used for synchronizing their trips. We develop a two-phase method (a pre-processing algorithm and a matching optimization problem) for assessing the sharing potential of different AV ownership models, and we evaluate them on a case study for New York City.Then, we present a model that integrates freight deliveries to a scheduled line for people transportation where passengers demand, and thus the available capacity for transporting freight, is assumed to be stochastic. We model this problem as a two-stage stochastic problem and we provide a MIP formulation and a sample average approximation (SAA) method along with an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it. We then analyze the proposed approach as well as the impacts of stochastic passengers demand on such integrated system on a computational study.Finally, we summarize the key findings, highlight the main challenges facing shared mobility systems, and suggest potential directions for future research.
Fleet operators rely on forecasts of future user requests to reposition empty vehicles and efficiently operate their vehicle fleets. In the context of an on-demand shared-use autonomous vehicle (AV) mobility service (SAMS), this study analyzes the trade-off that arises when selecting a spatio-temporal demand forecast aggregation level to support the operation of a SAMS fleet. In general, when short-term forecasts of user requests are intended for a finer space–time discretization, they tend to become less reliable. However, holding reliability constant, more disaggregate forecasts provide more valuable information to fleet operators. To explore this trade-off, this study presents a flexible methodological framework to evaluate and quantify the impact of spatio-temporal demand forecast aggregation on the operational efficiency of a SAMS fleet. At the core of the methodological framework is an agent-based simulation that requires a demand forecasting method and a SAMS fleet operational strategy. This study employs an offline demand forecasting method, and an online joint AV-user assignment and empty AV repositioning strategy. Using this forecasting method and fleet operational strategy, as well as Manhattan, NY taxi data, this study simulates the operations of a SAMS fleet across various spatio-temporal aggregation levels. Results indicate that as demand forecasts (and subregions) become more spatially disaggregate, fleet performance improves, in terms of user wait time and empty fleet miles. This finding comes despite demand forecast quality decreasing as subregions become more spatially disaggregate. Additionally, results indicate the SAMS fleet significantly benefits from higher quality demand forecasts, especially at more disaggregate levels.
In this paper, we propose a novel, computational efficient, dynamic ridesharing algorithm. The beneficial computational properties of the algorithm arise from casting the ridesharing problem as a linear assignment problem between fleet vehicles and customer trip requests within a federated optimization architecture. The resulting algorithm is up to four times faster than the state-of-the-art, even if it is implemented on a less dedicated hardware, and achieves similar service quality. Current literature showcases the ability of state-of-the-art ridesharing algorithms to tackle very large fleets and customer requests in almost near real-time, but the benefits of ridesharing seem limited to centralized systems. Our algorithm suggests that this does not need to be the case. The algorithm that we propose is fully distributable among multiple ridesharing companies. By leveraging two datasets, the New York city taxi dataset and the Melbourne Metropolitan Area dataset, we show that with our algorithm, real-time ridesharing offers clear benefits with respect to more traditional taxi fleets in terms of level of service, even if one considers partial adoption of the system. In fact, e.g., the quality of the solutions obtained in the state-of-the-art works that tackle the whole customer set of the New York city taxi dataset is achieved, even if one considers only a proportion of the fleet size and customer requests. This could make real-time urban-scale ridesharing very attractive to small enterprises and city authorities alike. However, in some cases, e.g., in multi-company scenarios where companies have predefined market shares, we show that the number of vehicles needed to achieve a comparable performance to the monopolistic setting increases, and this raises concerns on the possible negative effects of multi-company ridesharing.
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.
In the realm of human urban transportation, many recent studies have shown that comparatively smaller fleets of shared autonomous vehicles (SAVs) are able to provide efficient door-to-door transportation services for city dwellers. However, because of the steady growth of e-commerce and same-day delivery services, new city logistics approaches will also be required to deal with last-mile parcel delivery challenges. We focus on modeling a variation of the people and freight integrated transportation problem (PFIT problem) in which both passenger and parcel requests are pooled in mixed-purpose compartmentalized SAVs. Such vehicles are supposed to combine freight and passenger overlapping journeys on the shared mobility infrastructure network. We formally address the problem as the share-a-ride with parcel lockers problem (SARPLP), implement a mixed-integer linear programming (MILP) formulation, and compare the performance of single-purpose and mixed-purpose fleets on 216 transportation scenarios. For 149 scenarios where the solver gaps of the experimental results are negligible (less than 1%), we have shown that mixed-purpose fleets perform in average 11% better than single-purpose fleets. Additionally, the results indicate that the busier is the logistical scenario the better is the performance of the mixed-purpose fleet setting.
The transport impacts of collection-delivery points (CDPs), as an alternative to home delivery, are rarely studied. As e-shopping becomes increasingly popular, trips to collect deliveries at CDP, especially by car travel, may generate a considerable amount of external effects, such as emissions. Therefore, this paper analysed the “picking up/leaving goods” trips selected from the Swedish National Travel Survey and jointly modelled the individuals’ mode choice and trip chaining decisions using a panel cross-nested logit model. The roles of trip chain characteristics, individual socio-demographics and land use characteristics on each trip chain and mode choice combination are investigated. The results indicate observed and unobserved heterogeneities of trip chaining and mode choice decisions among populations. Young adults living with partners/spouses, single adults with children and partnered adults with children have the preference of using cars in collection-delivery trips compared to other life-cycle groups. A sensitivity analysis is carried out to estimate the effect of distance to CDPs on vehicle kilometres travelled. The calibrated model is used to estimate the VKT of collection-delivery trips in the greater Stockholm area. The results indicate a 22.5% reduction of VKT from collection-delivery trips by relocating 5% CDPs from urban areas to suburban and rural areas.
Studies about autonomous taxis (aTaxis) concluded that empty vehicle movements account for a share of about 10% of the total fleet mileage. In most of these studies, static traffic representations are used, neglecting the effect of the aTaxis on the flows and velocities in the network. Moreover, these static representations do not consider traffic delays due to traffic lights, turns, or incidents, for example. To address these two issues, the operation of an (electric) aTaxi system is implemented into an existing calibrated traffic microsimulation model of the city of Munich. On the one hand, a microsimulation depicts travel times more accurately than constant link-level values. On the other hand, the taxi movements influence the flows along the network links and thereby have the potential to change travel times and the rest of the traffic system significantly. Both the fleet operation module and the interface of Aimsun and the aTaxi module are illustrated. Some selected scenarios are simulated. In these scenarios, 10% of the private vehicle trips starting and ending inside the area of Munich’s highway belt are removed and completely or partially replaced by requests to the aTaxi system. The 1-to-1 substitution examines the influence of the extra empty vehicle movements. The partial replacement represents a changed mobility behavior and mode shifts, which could be a consequence of less vehicle ownership. Network-wide delay times increase only by a small amount in case of a 1-to-1 substitution of private vehicle trips to aTaxi requests and already decrease in case 10% of the omitted trips are assumed to be served by other means, e.g. public transportation. Additionally, the results from the aTaxi system in the microsimulation are compared to two models based on static traffic representations. In the first model, link-level travel times are constant throughout the day, while the second uses link-level travel times that are updated every 5 minutes. The travel-time values originate from the 5-minute averages of link-level travel times from the existing traffic microsimulation without the autonomous taxi system. Results indicate that time-constant link-level travel times underestimate the traffic in peak hours and therefore produce too optimistic customer waiting times. Moreover, the taxi fleet operation in the microsimulation also produces higher waiting times than the simulation with a time-dependent static traffic representation. Although average velocities are similar in both cases, simulations using lane- and time-averaged velocities do not show delays (e.g. due to a left turn at an intersection at a certain point in the 5-minute interval) in the same magnitude as the microsimulations.
While the sales volume of e-commerce transactions is growing rapidly, the traditional concept of packages delivery has been challenged by innovative approaches such as crowdsourced delivery. Using individuals, for example commuters, to deliver packages from senders to receivers can provide several economic and environmental benefits. This paper illustrates an algorithm that automates and optimizes the assignment of drivers to transportation requests by matching them based on transportation routes and time constraints. We evaluated our algorithm by using a simulated setting based on mobility data recorded in a major German city. This paper contributes to theory by giving guidance for future research on matching algorithms for crowdsourced delivery systems and to practice by illustrating an algorithm that can be adapted by existing and new crowdsourced delivery platforms.
This paper proposes a crowdsource-enabled system for urban parcel relay and delivery. We consider cyclists and pedestrians as crowdsources who are close to customers and interested in relaying parcels with a truck carrier and undertaking jobs for the last-leg parcel delivery and the first-leg parcel pickup. The crowdsources express their interests in doing so by submitting bids to the truck carrier. The truck carrier then selects bids and coordinates crowdsources’ last-leg delivery (first-leg pickup) with its truck operations. The truck carrier's problem is formulated as a mixed integer non-linear program which simultaneously i) selects crowdsources to complete the last-leg delivery (first-leg pickup) between customers and selected points for crowdsource-truck relay; and ii) determines the relay points and truck routes and schedule. To solve the truck carrier problem, we first decompose the problem into a winner determination problem and a simultaneous pickup and delivery problem with soft time windows, and propose a Tabu Search based algorithm to iteratively solve the two subproblems. Numerical results show that this solution approach is able to yield close-to-optimum solutions with much less time than using off-the-shelf solvers. By adopting this new system, truck vehicle miles traveled (VMT) and total cost can be reduced compared to pure-truck delivery. The advantage of the system over pure-truck delivery is sensitive to factors such as penalty for servicing outside customers’ desired time windows, truck unit operating cost, time value of crowdsources, and the crowdsource mode.
'The last mile' is a phrase commonly used in a logistics and freight transport context to highlight the special characteristics and challenges in moving goods within urban areas in what is usually the last leg of a long freight transport chain. The last mile challenge often also involves changing mode of transport for that leg, categorizing the transport chain as intermodal. Although similar issues arise for passenger transport, integrated transport is more commonly used to highlight the need to change modes of transport for the last, or fi rst, section of a long-distance public transport journey. Although passenger and goods transport share the same infrastructure, predominantly in urban areas, they are largely seen as diff erent systems and remain separated. Thus wasting scarce resources and contributing to congestion and the 'last mile' problem. In this context, the aim of this paper is to synthesize the main issues related to the fi rst and last mile in freight and passenger transport and, based on that, to explore synergies between them in order to share the use of resources-based on time, space and vehicle. To this end, various examples of sharing resources for transport of passengers and freight are provided. The paper concludes that integrating passenger and freight transport in urban areas is a promising approach to easing the last mile problem. However, to advance operational integration of passenger and freight transport services, integration at the institutional and business levels of freight and passenger transport provision is required.
Significance
Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min.
Sharing rides could drastically improve the efficiency of car and taxi transportation. Unleashing such potential, however, requires understanding how urban parameters affect the fraction of individual trips that can be shared, a quantity that we call shareability. Using data on millions of taxi trips in New York City, San Francisco, Singapore, and Vienna, we compute the shareability curves for each city, and find that a natural rescaling collapses them onto a single, universal curve. We explain this scaling law theoretically with a simple model that predicts the potential for ride sharing in any city, using a few basic urban quantities and no adjustable parameters. Accurate extrapolations of this type will help planners, transportation companies, and society at large to shape a sustainable path for urban growth.
Despite the great demand on and attempts at pack- age express shipping services, online retailers have not yet had a practical solution to make such services profitable. In this paper, we propose an economical approach to express package delivery, i.e., exploiting relays of taxis with passengers to help transport package collectively, without degrading the quality of passenger services. Specifically, we propose a two-phase framework called CROWDDELIVER for the package delivery path planning. In the first phase, we mine the historical taxi trajectory data offline to identify the shortest package delivery paths with estimated travel time given any Origin-Destination pairs. Using the paths and travel time as the reference, in the second phase, we develop an online adaptive taxi scheduling algorithm to find the near optimal delivery paths iteratively upon real-time requests, and direct the package routing accordingly. Finally, we evaluate the two-phase framework using the real-world datasets, which consist of POI, road network, and the large-scale trajectory data respectively generated by 7,614 taxis in a month in the city of Hangzhou, China. Results show that, over 85% of packages can be delivered within 8 hours, with around 4.2 relays of taxis on average.
Integrating freight flows with scheduled public transportation services creates attractive business opportunities as the same transportation needs can be met with fewer operating costs. The pickup and delivery problem with time windows and scheduled lines (PDPTW-SL) aims at routing a given set of vehicles to transport freight requests from their origins to their corresponding destinations, where the requests can use scheduled passenger transportation services as a part of their journeys. We describe the PDPTW-SL as an arc-based mixed-integer program. Computational results on a set of small-size instances provide a clear understanding of the benefits of using scheduled line services as a part of freight's journey.
Integrating passenger and freight transport systems, known as co-modality, is becoming more feasible due to recent developments in information and communication technologies (ICT) such as smart phones and global position systems (GPS). This paper uses simulation of an on-demand transportation scheme in which passengers and parcels can travel together to explore the benefits of co-modality when compared to existing schemes. It is shown that, depending on the demand, co-modality can provide improved experiences for both operators and passengers/customers.
The integration of passenger and freight transport has been the subject of debate among scholars from the beginning of the century, with a peak observed in the last five years. Considering the relatively recent interest in the topic, most authors have highlighted the heterogeneous and explorative approaches adopted so far, indicating a lack of systematic analyses. This is confirmed by the different names given to the same concept (e.g., co-modality, cargo hitching, system with mixed passengers and goods, share-a-ride, integrated passenger and freight logistics, and collaborative passenger and freight transport). This study conducts a comprehensive literature review based on the scientific contributions indexed in Scopus and selected through a semiautomatic data extraction. First, a descriptive analysis is conducted, which includes types of publication, geographical areas, sources of publication, research methods, and research design adopted. Then, a text mining analysis identifies the main content-related aspects, including a semantic investigation of the most frequently occurring terms, their clustering in homogeneous groups, the transport means considered, and the territorial scales that have been investigated. This analysis is used to define the future challenges related to the topic, which span from the provision of more robust quantitative analyses (studies providing real data and adopting ad-hoc models are still very limited) to policy-related issues. However, the definition of a normative framework that integrates both systems is essential to deal with passenger–freight transport in a combined manner.
On-Demand Mobility (ODM) has appeared in recent years in almost all places on earth. Notably, in large cities,
it is no longer possible to imagine passenger transport without
it. In the course of this development, the costs for this form of
mobility have been anything but constant and transparent.
However, in recent years, prices stabilized and now permit a detailed cost analysis. This paper analyzes the topic of ride-hailing
cost composition and develops and quantifies cost components
for the case of a premium ride-hailing provider in Munich. For
a complete analysis, costs are investigated for Fleet Managers
and ODM Providers separately, taking into account vehicle,
overhead, and platform costs. In addition, different scenarios
quantifying the influence of fleet sizes, total mileage, and trips,
as well as the emerging trends of fleet electrification and automation, are investigated. The results show the high fixed costs of
Fleet Managers, who should aim for high utilization to achieve
positive economies of scale. Electric and autonomous fleets indicate cost-saving potential for Fleet Managers, respectively, due
to favorable regulation or eliminated driver costs. The results
also show the low fixed asset share of ODM Providers, making
it easy for them to scale up.
This paper develops an operational strategy in which urban rail transit is used for freight transport. An environment-friendly urban freight transportation alternative is analyzed by employing optimization techniques to support the collaborative transportation of passengers and freight. Practical cases are investigated to test the technical feasibility of this transportation scheme. The paper formulates the train service design problem on a single urban rail line with passenger and freight. Passenger trains have a prescribed timetable which is allowed to be slightly modified to facilitate the freight service. Freight can be transported by inserting dedicated freight trains or utilizing the extra space inside the passenger train carriages. Station platforms are able to load and unload both goods and passengers. An optimization model for combined train service design is proposed to maximize profit resulting from the balance of revenues and costs brought by the freight service. The efficient schedules of trains and freight allocation plans are to be determined. This problem is formulated as a mixed-integer linear programming model. An iterative scheduling approach which includes a pre-processing method and two heuristic iterative algorithms is designed to solve the model. Two numerical examples are introduced to demonstrate the efficiency of the proposed model and the iterative scheduling approach.
The growth in online goods delivery is causing a dramatic surge in urban vehicle traffic from last-mile deliveries. On the other hand, ride-sharing has been on the rise with the success of ride-sharing platforms and increased research on using autonomous vehicle technologies for routing and matching. The future of urban mobility for passengers and goods relies on leveraging new methods that minimize operational costs and environmental footprints of transportation systems. This article considers combining passenger transportation with goods delivery to improve vehicle-based transportation. We propose FlexPool: a distributed model-free deep reinforcement learning algorithm that jointly serves passengers & goods workloads by learning optimal dispatch policies from its interaction with the environment. The proposed algorithm pools passengers for a ride-sharing service and delivers goods using a multi-hop transit method. These flexibilities decrease the fleet's operational cost and environmental footprint while maintaining service levels for passengers and goods. The dispatching algorithm based on deep reinforcement learning is integrated with an efficient matching algorithm for passengers and goods. Through simulations on a realistic multi-agent urban mobility platform, we demonstrate that FlexPool outperforms other model-free settings in serving the demands from passengers & goods. FlexPool achieves 30% higher fleet utilization and 35% higher fuel efficiency in comparison to (i) model-free approaches where vehicles transport a combination of passengers & goods without the use of multi-hop transit, and (ii) model-free approaches where vehicles exclusively transport either passengers or goods.
Aerial ropeways or cableways are a common means of transport in mountainous regions. Quite unusual is a ropeway combining both freight and passenger transport. Such a system provides a supplementary solution to arising logistical problems in areas with dedicated spatial challenges. A simulation study for a potential application linking two infrastructural nodes in an alpine tourist region was used to analyse the performance of such a system. Various system configurations and operating strategies for controlling the freight transport were examined. The major findings show the interrelated effects on freight and passenger transportation capacities and the trade-off between them. The simulation model can easily be adapted and transferred to other potential applications.
The “First-Last Mile” problem (FLM) is a relevant transport issue. According to the Green Paper on Urban Mobility, the combination of passenger and freight flows may be a valid approach to promote sustainable, efficient and socially desirable FLM transport. This paper proposes a set of key performance indicators to evaluate potential improvements in operational, environmental and social performances of integrated passenger and freight flows, compared to the current transport schemes. The two case studies of the Northern lagoon of Venice in Italy and the Slovenian Municipality of Velenje indicate that the model may be particularly effective in those cases where reduced freight volumes, limited freight pickup/delivery locations and a lower elasticity of the travel demand reduce the constraints to the adoption of this integrated scheme. Results call for a reduction both in distances travelled and in externalities produced, and hence a good potential in FLM terms. Beside these technical aspects, one of the main issues is the need for innovation in the normative/regulatory context: a prerequisite in order to apply this solution to real-life contexts.
Using the same vehicles for both passenger and freight transport, to increase vehicle occupancy and decrease their number, is an idea that drives transport planners and is also being addressed by manufacturers. This paper proposes a methodology to simulate the behavior of such vehicles within an urban traffic system and evaluate their performance. The aim is to investigate the impacts of resignation from fleet ownership by a transport service company (TSC) operating on a city-wide scale. In the simulation, the service provider hires private autonomous cars for tour performance. Based on assumptions concerning the operation of such vehicles and TSCs, the software Multi-Agent Transport Simulation (MATSim) is extended to model vehicle and operator behavior. The proposed framework is applied to a case study of a parcel delivery service in Berlin serving a synthetic parcel demand.
Results suggest that the vehicle miles traveled for freight purposes increase because of additional access and egress trips. Moreover, the number of vehicles en route is higher throughout the day. The lowering of driver costs can reduce the costs of the operator by approximately 74.5%. If the service provider additionally considers the resignation from fleet ownership, it might lower the operation cost by another 10%, not taking into account the costs of system transfer or risks like vehicle non-availability. From an economic perspective, the reduction of the overall number of vehicles in the system seems to be beneficial.
In unit-capacity mobility-on-demand systems, the vehicles transport only one travel party at a time, whereas in ride-sharing mobility-on-demand systems, a vehicle may transport different travel parties at the same time, e.g., if paths are partially overlapping. One potential benefit of ride sharing is increased system efficiency. However, it is not clear what the trade-offs are between the efficiency gains and the reduction in quality of service. To quantify those trade-offs, an open-source simulation environment is introduced, which is capable of evaluating a large class of operational policies for ride-sharing mobility-on-demand systems. The impact of ride sharing on efficiency and service level is assessed for several benchmark operational policies from the literature and for different transportation scenarios: first a dense urban scenario, then a line-shaped, rural one. Based on the results of these case studies, we find that the efficiency gains in ride sharing are relatively small and potentially hard to justify against quality of service concerns such as reduced convenience, loss of privacy, and higher total travel and drive times. Furthermore, in the assessed scenarios, the relatively low occupancy of the vehicles suggests that smaller vehicles with 4-6 seats, able to handle occasional ride sharing, may be preferable to larger and more expensive vehicles such as minibuses.
Autonomous on-demand mobility systems, especially
ride-pooling services, except for providing convenient
transportation for the people, could potentially improve the
traffic congestion in urban environments by reducing the
number of private vehicles. In this paper, we introduce an
Autonomous On-Demand Ride-Pooling (AODRP) system, which
uses a rather realistic customer-model that is sensitive to waiting
times. To quantify the benefits that the AODRP system could
have on a city network, a case study in Munich is performed
with a shared fleet of vehicles. Different scenarios, in which
private vehicle trips are partly replaced with ride-pooling trips
until an adoption rate of 15%, are investigated for varying
allowed customer detour times. The results show that the
benefits of an AODRP service are observed from a certain
adoption rate. For low demand level of 1%, the ride-pooling
service even increases Vehicle Miles Traveled (VMT) in the
system, due to the empty trips generated while going to pick
up customers. For higher adoption rates, pooling makes up for
the additional empty VMT starting from approximately 5%
adoption rate. An analysis of change in VMT per road type
reveals that the AODRP system especially reduces traffic on
major roads, in which nowadays the highest level of congestion
is observed, while extra VMT due to empty pick-up trips are
concentrated on minor roads.
Crowdsourced delivery systems have created many new industry last-mile delivery solutions and have received some attention in the academic literature. In this review paper, we analyze the current industry status of this emergent concept and provide a classification of available platforms based on their matching mechanisms, target markets and compensation schemes. We review the operations research (OR) literature explicitly addressing this topic and assess the realism of the assumptions, and applicability to real-world applications. We also compare the management decisions within crowdsourced delivery systems to well-studied OR problems in the literature, and pinpoint new challenges that arise in the context of crowdsourced delivery. The purpose of this paper is to identify key elements of such systems that distinguish them from other transportation systems, to ultimately shed light on some promising research directions.
City-wide package delivery becomes popular due to the dramatic rise of online shopping. It places a tremendous burden on the traditional logistics industry, which relies on dedicated couriers and is labor-intensive. Leveraging the ridesharing systems is a promising alternative, yet existing solutions are limited to one-hop ridesharing or need consignment warehouses as relays. In this paper, we propose a new package delivery scheme which takes advantage of multi-hop ridesharing and is entirely consignment free. Specifically, a package is assigned to a taxi which is guided to deliver the package all along to its destination while transporting successive passengers. We tackle it with a two-phase solution, named PPtaxi. In the first phase, we use the Multivariate Gauss distribution and Bayesian inference to predict the passenger orders. In the second phase, both the computation efficiency and solution effectiveness are considered to plan package delivery routes. We evaluate PPtaxi with a real-world dataset from an online taxi-taking platform and compare it with multiple benchmarks. The results show that the successful delivery rate of packages with our solution can reach 95 higher than those of the benchmarks.
When considering autonomous mobility on demand (AMoD) trends, it is probably safe to assume that they will have a large market share in the near future. In the introductory phases, users of current non-autonomous mobility on demand services such as ride-hailing and carsharing are expected to be among the first users of AMoD systems. The research presented in this paper aims to estimate fares for an AMoD system in this early stages based on rental and financial data provided by a free-floating CS provider. It demonstrates that an autonomous taxi (aTaxi) model requires less vehicles to serve the same demand resulting in the possibility to lower fares. In our model, user behavior is represented by defining three maximal waiting times and three monetary values reflecting their dissatisfaction in the case, where they cannot be served in due time. Two bipartite optimization problems for vehicle-to-user and relocation assignments build the core of the introduced aTaxi model. Fleet size and relocation parameters are chosen according to a utility function representing profit and opportunity costs of users not being served. We compute the reduction in fares to break-even with the current CS profit. Results of a case-study in Munich, Germany, indicate that one aTaxi can replace 2.8-3.7 CS vehicles. The aTaxi operator can therefore reduce fares by 29%-35% to achieve the same profit assuming the same cost structures as in free-floating CS.
The trend toward shorter delivery lead times reduces operational efficiency and increases transportation costs for Internet retailers. However, mobile technology creates new opportunities to organize the last mile. In this paper, we study the concept of crowdsourced delivery that aims to use excess capacity on journeys that already take place. We consider a service platform that automatically creates matches between parcel delivery tasks and ad hoc drivers. The platform also operates a fleet of dedicated vehicles to serve the tasks that cannot be served by the ad hoc drivers. The matching of tasks, drivers, and dedicated vehicles in real time gives rise to a new variant of the dynamic pickup and delivery problem. We propose a rolling horizon framework and develop an exact solution approach to solve the matching problem each time new information becomes available. To investigate the potential benefit of crowdsourced delivery, we conduct a wide range of computational experiments. The experiments provide insights into the viability of crowdsourced delivery under various assumptions about the behavior of the ad hoc drivers. The results suggest that the use of ad hoc drivers has the potential to make the last mile more cost-efficient and can provide system-wide vehicle-mile savings up to 37% compared to a traditional delivery system with dedicated vehicles.
The online appendix is available at https://doi.org/10.1287/trsc.2017.0803 .
This paper addresses a real-life problem arising in the ongoing “Grand Paris” project. We investigate an environment-friendly urban freight transportation alternative using passenger rail network, by providing a decision support tool for decision makers to assess the technical feasibility, the impact on services to passengers, the needs in infrastructure and hence in investment. We identify relevant scientific issues that need to be addressed in this topic at strategical, tactical and operational levels. Then we focus on the Freight-Rail-Transport-Scheduling Problem which provides valuable information to and constitutes a basis for other related problems. This problem is first formulated into a MIP. We prove its NP-hardness and hence propose a heuristic based on dispatching rules and a single-train-based decomposition heuristic. The performances of these heuristics are evaluated via employing a discrete-event simulation approach, which also provides a general framework which supports decision-makers in modelling and evaluating the dynamics of such a system for various alternative solutions under various scenarios.
Two socioeconomic transformations, namely, the booms in the sharing economy and retail e-commerce, lead to the prospect where shared mobility of passenger cars prevails throughout urban areas for home delivery services. Logistics service providers as well as local governments are in need of evaluating the potentially substantial impacts of this mode shift, given their economic objectives and environmental concerns. This paper addresses this need by providing new logistics planning models and managerial insights. These models characterize open-loop car routes, car drivers’ wage-response behavior, interplay with the ride-share market, and optimal sizes of service zones within which passenger vehicles pick up goods and fulfill the last-mile delivery. Based on theoretical analysis and empirical estimates in a realistic setting, the findings suggest that crowdsourcing shared mobility is not as scalable as the conventional truck-only system in terms of the operating cost. However, a transition to this paradigm has the potential for creating economic benefits by reducing the truck fleet size and exploiting additional operational flexibilities (e.g., avoiding high-demand areas and peak hours, adjusting vehicle loading capacities, etc.). These insights are insignificantly affected by the dynamic adjustment of wages and prices of the ride-share market. If entering into this paradigm, greenhouse gas emissions may increase because of prolonged car trip distance; on the other hand, even exclusively minimizing operating costs incurs only slightly more emissions than exclusively minimizing emissions.
The online appendix is available at https://doi.org/10.1287/msom.2017.0683 .
Studies about autonomous taxis (aTaxis) concluded that empty vehicle movements account for a share of about 10% of vehicle miles traveled. In many of these studies, constant (in time or space) travel times are used due to a lack of data and to simplify the computations. Furthermore, the influence of the empty vehicle movements on the street network has not yet been a focal point of research.
To address these two issues, the operation of an aTaxi system is implemented into an existing traffic microsimulation model. On the one hand, a microsimulation depicts travel times more
accurately than constant link-level values. On the other hand, the taxi movements influence the flows along the network links and thereby have the potential to change travel times in the street network.
Based on a calibrated Aimsun model of the city of Munich, a small number of scenarios are simulated: Starting from the calibrated OD matrix, a share of 10% of trips originating and ending inside the highway belt of Munich are completely or partially served by aTaxis.
In case of a 1-to-1 substitution of private trips with aTaxi requests, the network-wide delay of private vehicles only increases by 1% due to induced empty rides.
Furthermore, the differences between a simulation using link-level travel times and a traffic microsimulation are studied. Delays due to left turns and traffic lights are present in the microsimulation. Results show, that fleet operation algorithms need to address these issues, which occur in reality.
Crowdsourcing is gathering increased attention in freight transport areas, mainly applied in internet-based services to city logistics. However, scientific research, especially methodology for application is still rare in the literature. This paper aims to fill this gap and proposes a methodological approach of applying crowdsourcing solution to Last Mile Delivery in E-commerce environment. The proposed solution is based on taxi fleet in city and a transport network composed by road network and customer self-pickup facilities that are 24/7 shops in city, named as TaxiCrowdShipping system. The system relies on a two-phase decision model, first offline taxi trajectory mining and second online package routing and taxi scheduling. Being the first stage of our study, this paper introduces the framework of the system and the decision model development. Some expected results and research perspectives are also discussed.
Freight on Transit (FOT) refers to an operational strategy where public transit vehicles and/or infrastructure are used to move freight. Examples include moving goods alongside passengers on buses, attaching cargo trailers to transit vehicles, and operating freight vehicles between trains on subway lines. This paper describes the methods and results of a three-round Delphi study engaging 34 transportation experts to explore challenges and opportunities of FOT and to conceptualize and evaluate potential FOT operations in Toronto. Traditional Delphi methods were used for the exploration of FOT challenges and opportunities, and a modified approach was formulated to integrate experts' opinions and develop new FOT concepts for Toronto. The results support previous claims that technical challenges of FOT may be easier to overcome than institutional barriers. Evaluation of potential FOT operating strategies in Toronto suggests that while the current public transit network does not have capacity to support additional movements, there may be realistic opportunities to include freight service in future projects as a means of offsetting operating costs and reducing the impacts of goods movements.
In many cities of the world, road space is increasingly contested. Growing vehicle numbers, traffic calming and the development of new infrastructure for more sustainable transport modes such as bicycles have all contributed to pressure on available space and conflicts over the allocation of space. This paper provides the first assessment of urban transport infrastructure space distribution, distinguishing motorized individual transport, public transport, cycling and walking. To calculate area allocation, an assessment methodology was developed using high-resolution digital satellite images in combination with a geographical information system to derive area measurements. This methodology was applied to four distinctly different city quarters in Freiburg, Germany. Results indicate that space is unevenly distributed, with motorized individual transport being the favoured transport mode. Findings also show that if trip number to space allocation ratios are calculated, one of the most sustainable transport modes, the bicycle, is the most disadvantaged. This suggests that area allocation deserves greater attention in the planning and implementation of more sustainable urban transport designs.