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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 del...
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... The main difference between the two models is that all requests are flexible in the SARP, but in the FIP, the routes are partially fixed beforehand based on passenger requests [4], which is expected to alleviate urban traffic congestion while providing new revenue streams for taxi companies. As research on the passenger and parcel Share-a-Ride problem deepened, Beirigo et al. [6] and van der Tholen et al. [7] later removed these two conditions in subsequent studies, making the problem more realistic. Beirigo et al. [6] focuses 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 (shared autonomous vehicles). ...
(1) Efficient resource utilization in urban transport necessitates the integration of passenger and freight transport systems. Current research focuses on dynamically responding to both passenger and parcel orders, typically by initially planning passenger routes and then dynamically inserting parcel requests. However, this approach overlooks the inherent flexibility in parcel delivery times compared to the stringent time constraints of passenger transport. (2) This study introduces a novel approach to enhance taxi resource utilization by proposing a shared model for people and parcel transport, designated as the SARP-LTW (Sharing a ride problem with loose time windows of parcels) model. Our model accommodates loose time windows for parcel deliveries and initially defines the parcel delivery routes for each taxi before each working day, which was prior to addressing passenger requests. Once the working day of each taxi commences, all taxis will prioritize serving the dynamic passenger travel requests, minimizing the delay for these requests, with the only requirement being to ensure that all pre-scheduled parcels can be delivered to their destinations. (3) This dual-service approach aims to optimize profits while balancing the time-sensitivity of passenger orders against the flexibility in parcel delivery. Furthermore, we improved the adaptive large neighborhood search algorithm by introducing an ant colony information update mechanism (AC-ALNS) to solve the SARP-LTW efficiently. (4) Numerical analysis of the well-known Solomon set of benchmark instances demonstrates that the SARP-LTW model outperforms the SARP model in profit rate, revenue, and revenue stability, with improvements of 48%, 46%, and 49%, respectively. Our proposed approach enables taxi companies to maximize vehicle utilization, reducing idle time and increasing revenue.
... Combined people and freight transportation systems have been gaining attention in the literature [3,9]. For example, solution approaches have been designed for settings where people and parcel share rides on taxis (see, e.g., [8]) and compartmentalized autonomous vehicles (see e.g., [1,13]). However, there is no study on optimizing the operations of a DCAV-based transportation system that dynamically switches transport units to fulfill heterogeneous demand. ...
... Constraints (2) to (6) are the general routing constraints, and (7) to (9) are spatial and temporal synchronisation constraints to couple the movement of carriers and modules. The time window constraints are described by constraints (10) to (13) and subsequently constraints (14) and (15) are the ride time constraints. Finally, constraints (16) to (18) are the vehicle load constraints. ...
... A vehicle should arrive at the destination vertex of a request pair after the pickup vertex has been visited, which is guaranteed by (12). Vehicles are not allowed to arrive early and wait at request pickup locations, constraint (13) ensures that for the set of arcs on which waiting is not allowed (A k W ) vehicles won't arrive before the earliest pickup time. Constraint (14) defines the ride time of a request, and (15) ...
A Dynamically Configurable Autonomous Vehicle (DCAV) is a new class of autonomous vehicle concept using a separable design of lower and upper parts—carriers and modules—to allow more flexible operation. A fleet of DCAVs consists of a set of carriers and a set of compatible modules. Different, possibly crowd-sourced, modules can increase the number of use-cases for DCAVs, possibly leading to disruptive changes in the transport sector. This study investigates the use of DCAV system operating on an Autonomous Mobility-on-Demand (AMoD) scenario, combining passenger and freight transport flows. The novel problem is denoted as the Dynamically Configurable Autonomous Vehicle Pickup and Delivery Problem (DCAVPDP). We propose a mixed-integer linear programming (MILP) model aiming to minimize DCAV fleet size and distance traveled. We compare the performance of a DCAV fleet to the performance of a typical single-purpose fleet (consisting of
dedicated passenger and freight vehicles). The numerical study, with 360 instances for each fleet type, considering four people-and-freight demand distribution scenarios, the inclusion of ridesharing, module-and-carrier (de)coupling locations, and different simulation horizon lengths, shows that the proposed modular DCAV system can fulfill a mixed people-and-freight demand using, on average, 18.77% fewer carriers than a regular AMoD system comprised of single-purpose vehicles while increasing on-duty fleet utilization by 4.82%.
... These regulations were gradually relaxed to present a more general SARP, leading to more profits. Beirigo et al. (2018) and Tholen et al. (2021) eliminated R2 and R3. Yu et al. (2018Yu et al. ( , 2022b relaxed all these regulations. ...
... MIP models and two-stage stochastic programming models are commonly used for deterministic problems and problems with uncertainty, respectively. Regarding solution approaches, commercial solvers such as CPLEX and Gurobi can solve small instances (Li et al., 2014;Beirigo et al., 2018;Tholen et al., 2021). Metaheuristics, e.g. ...
The promotion of urban mobility by integrating people-and-goods transportation has attracted increasing attention in recent years. Within this framework, diversified forms such as co-modality, freight on transit, and crowdshipping have been proposed, piloted or implemented. The success of the implementation and market penetration depends on not only the novelties of the concept but also the planning and operational efficiency. Thus, a comprehensive review focusing on the operation of integrated people-and-goods transportation systems and associated critical decisions and subproblems is performed. Different practical forms in which people and goods are transported in an integrated manner are identified. The critical decisions associated with each form and subproblem are discussed, along with corresponding models and solution approaches. Notably, because integrated transportation systems are in the early exploration stage at present, new forms are expected to emerge. Therefore, this paper proposes a general framework to realise the planning and operation of new forms in the future. The decisions and subproblems identified from existing forms are fed to the proposed general framework to identify two key research opportunities: to improve or extend existing research and to conduct pioneering research to fill the gaps in the frameworks for operating potential forms of integrated people-and-goods transportation.
... Furthermore, robots constitute the ideal agents for last-mile delivery, as they can conduct contactless deliveries, especially useful in pandemic situations [19]. Shared autonomous vehicles (SAV) [20,21] are capable of conveying people and goods to their final destination. Therefore, they are appropriate for conducting last-mile delivery services, while it has been shown that mixed people and goods missions tend to be on average about 11% more efficient than single-function ones [20]. ...
The occurring growth in e-commerce comes along with an increasing number of first-time delivery failures due to the customer’s absence at the delivery location. Failed deliveries result in rework, causing a significant impact on the carriers’ delivery cost. Hence, the last mile is the portion of a journey that involves moving people and commodities from a transportation hub to a final destination, which should be an efficient process. The above-mentioned concept is used in supply chain management and transportation planning. The paper at hand is a position paper that aims to scrutinize the concept of driverless last-mile delivery, with autonomous vehicles, in order to highlight and stress the challenges and limitations in the existing technology that hinder level five autonomous driving. Specifically, this work documents the current capabilities of the existing autonomous vehicles’ perception and cognition system and outlines their future skills towards addressing complete autonomous last-mile delivery, as well as efficient robotic process automation in logistics from warehouse/distribution center to hub’s delivery.
... They investigated two modes of operation; Intelligent connected mode and Traction mode. Tholen et al. [44] optimized the capacity of ondemand modules of passenger and parcel compartments onboard shared autonomous vehicle (SAV) used in urban transportation. Fielbaum [45] studied a feeder system that operates on-demand in a local zone. ...
Autonomous vehicles (AV) have gained ground in recent years. However, they still use the principles of traditional vehicles in terms of design and operation. This work proposes an adaptive transportation system based on autonomous POD vehicles, and investigates a major aspect of its operation. The PODs used in the proposed system can be considered a variant version of existing autonomous PODs. However, their unique design and concept of operation enable them to operate more efficiently than existing PODs. The proposed system involves docking and undocking of these PODs based on passengers’ demands. However, during the merging process, undesired collisions could happen due to unforeseen conditions. If the approach speed is high enough, it could induce damage in to the vehicles. This work investigates some possible scenarios of the potential collisions that could happen between these PODs during the merging process. Based on these scenarios, the allowed safe approach speeds are determined. These speeds can help in designing the operation of the proposed transportation system. Some of the variables considered in this work include; type of body material, shell thickness, impact speed, stress, deformation, and absorbed energy. The safe design merging speeds have been determined under different conditions.
... Their results suggest, that the vehicle miles traveled for freight purposes increase due to additional access and egress trips. Van der Tholen et al. [63] present a method to estimate the optimal capacity for the passenger and parcel compartments of AMoD systems. They aim at creating an optimal routing schedule between a randomly generated set of pick-up and drop-off requests of passengers and parcels. ...
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. 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 transport. 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 deceased compared to the status quo.
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.