Multiple Trips Research Articles

Strategies at the platform-train interface: in the pursuit of psychological comfort?

ABSTRACT Platform-train interface (PTI) is a shared space with several interactions between travellers. These interactions lead to congestion, delays, safety concerns at PTI and resulting in a negative experience for users. Understanding individual behaviour is crucial to improving pedestrian flow at stations and users’ experience, to fine supporting public transport use. This study aims to understand how individuals experience and behaviour at PTI. Specifically, we investigate psychological comfort and its link with approach/avoidance strategies (AAS) used by travellers at PTI. Based on the critical incidents technique, 22 interviews were conducted. Participants were asked to describe multiple trips according to their valence (positive v. negative). Seventy-seven critical incidents representing 125 route sections involving a station were collected, coded and analysed through multiple correspondence analysis (MCA), to determine reasons of incidents’ valence. The descriptive analysis of the 125 sections aims at identifying AAS used by individuals at PTI. Results suggest that reasons of the valence of the trip (e.g., security or density) could be related to the different psychological comfort’s dimensions. AAS reported seem to be aimed at increasing psychological comfort. Finally, we observed that while crowds usually link to avoidance strategies, in specific cases of uncertainty travellers also reported approaching a crowd.

Are Clinically Node-Negative Patients with a Positive Preoperative Axillary Lymph Node Biopsy Appropriate Candidates for Sentinel Lymph Node Biopsy?

Whether cN0 patients with image-detected nodal metastases are appropriate for sentinel lymph node biopsy (SLNB) or should proceed directly to axillary lymph node dissection (ALND) or neoadjuvant chemotherapy (NAC) is controversial. We sought to determine how often ALND is needed with upfront surgery and to identify factors associated with ≥ 3 positive SLNs after a positive preoperative lymph node (LN) biopsy. Patients with cT1-2N0 breast cancer and a positive LN biopsy treated from 2014 to 2022 were identified from a prospective database. Patients who received NAC were excluded. Clinicopathologic characteristics were compared between women with 1-2 positive SLNs and ≥ 3 positive SLNs. Of 90 eligible patients, 66 (73%) had 1-2 positive SLNs and 24 (27%) had ≥ 3 positive SLNs. The median patient age was 62 years, median tumor size was 2.2 cm, and 16 women (18%) received a mastectomy. There was no difference in body mass index, tumor size, histology, grade, multifocality, presence of lymphovascular invasion, and receptor status between groups. On multivariable analysis, having ≥ 3 positive SLNs was associated with > 1 abnormal LN on preoperative imaging (odds ratio [OR] 4.36, 95% confidence interval [CI] 1.47-14.0; p = 0.01), microscopic extracapsular extension in the SLNs (OR 3.83, 95% CI 1.25-13.7; p = 0.025), and a higher median number of SLNs removed (OR 1.42, 95% CI 1.10-1.88; p = 0.01). More than 70% of women with cT1-2 breast cancer with image-detected nodal metastases had < 3 positive SLNs and did not require ALND. To avoid multiple trips to the operating room, frozen section can be considered in women with multiple abnormal LNs on imaging.

Optimizing customized bus services for multi‐trip urban passengers: A bi‐objective approach

AbstractCustomized bus services typically focus on single‐trip requests, which often struggle to accommodate the growing needs for varied multiple trips in urban daily travel. This paper addresses the customized bus routing problem for passengers with multiple trips. A bi‐objective mathematical model is established for maximizing the operational profit and minimizing the travel costs by considering the characteristics of the multi‐trip requests and time‐dependent travel time. Besides, a novel profit objective function is proposed considering the service's completion status and the starting price. Since the proposed mixed integer linear programming model is an NP‐hard problem, a non‐dominated sorting genetic algorithm II‐based method is proposed to handle different sizes of instances. Finally, the instances with multi‐trip requests are carried out to test the accuracy of the model and the effectiveness of our method compared with Gurobi and the local search‐based multi‐objective algorithm approach.

A dynamic discrete choice modelling approach for forward-looking travel mode choices

In this paper, we present a systematic approach based on dynamic discrete choice models (DDCM) to investigate individuals’ forward-looking mode choice behaviours in daily travel tours with multiple destinations. We propose a novel network transformation model that encompasses the entire decision space of all feasible mode combinations for every observed trip/tour in the dataset. By applying the well-established Recursive Logit model structure commonly used in path choice modelling, we address the tour mode choice problem effectively and quantify forward looking considerations in the mode choice process. The proposed model captures the complex considerations individuals take into account when making mode choices. The network transformation incorporates downstream mode limitations into the preceding mode choice options, enabling us to model individuals’ forward-looking behaviour and gain insights into how considerations of future trips such as a shopping in the evening, or school pick-up trip influence previous mode choice decisions earlier in the day. Uncovering and quantifying these hidden forward-looking factors can help modellers better explain the private car usage usually observed for the entire sequences of daily trips, even in presence of competitive alternative modes. The proposed network transformation also enables us to measure the effect of the requirement/preference to return private vehicles (car, motorcycle, and bicycle) home on mode choices in home-bound trips, and subsequently, on the entire daily mode choice decisions. To validate the proposed model, we utilise the VISTA household travel survey data from the Melbourne Metropolitan area in Australia. The model is compared against baseline models, demonstrating its statistical advantages. Additionally, intuitive illustrations using the data showcase the model’s efficacy. From transport planning and policy perspective, tour-based mode choice modelling provides a more comprehensive and precise understanding of travel behaviour by considering the sequence of trips made by an individual. This can help capture the interactions and dependencies between different trips, which trip-based models may overlook. The proposed model is more suitable for analysing the effects of policy interventions like congestion pricing, public transport investments, or new mobility initiatives, as they can better represent the cascading effects of such policies across multiple trips.

Cycle route uptake and scenario estimation (CRUSE): an approach for developing strategic cycle network planning tools

This paper describes an approach for developing strategic cycle network planning tools. Based on our experience developing and deploying the Cycle Route Uptake and Scenario Estimation (CRUSE) Tool for Ireland, we outline the underlying methods, including disaggregation of origin–destination data with the open source ‘odjitter’ software, incorporation of additional trip purposes, routing, scenario generation, and development of an intuitive user interface that is tested and used by practitioners. Commissioned by the national infrastructure agency Transport Infrastructure Ireland, CRUSE provides estimates of current and potential future cycling levels under ‘snapshot’ scenarios to inform investment decisions. The publicly available results at https://cruse.bike/ enable planners, engineers, and other stakeholders to make more evidence-based decisions. CRUSE goes beyond previous work by: modeling networks at high spatial resolution; simulating multiple trip purposes (social, shopping, personal utility, recreational, and cycle touring), supplementing official origin–destination datasets on travel for work and education; and providing estimates of ‘quietness’ (a proxy for cyclist comfort and route preference) at the route segment level. Three network types—‘Fastest’, ‘Balanced’, and ‘Quietest’—help plan both arterial and residential cycle networks. Workshops with stakeholders were used to inform the development of the tool. Feedback shows that the tool has a wide range of uses and is already being used in practice to inform urban, inter-urban, and rural cycle network designs. The approach is flexible and open source, allowing the underlying ideas and code to be adapted, supporting more evidence-based and effective cycling policies and interventions internationally.

Heterogeneous Electric Vehicle Routing Problem with Multiple Compartments and Multiple Trips for the Collection of Classified Waste

Heterogeneous Electric Vehicle Routing Problem with Multiple Compartments and Multiple Trips for the Collection of Classified Waste

Solving Dynamic Full-Truckload Vehicle Routing Problem Using an Agent-Based Approach

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders.

An inventory-routing optimization model for pharmaceutical supply chain with time-dependent travel time on a multi-graph network: a case study

PurposeSpurred by the high turnover in the pharmaceutical industry, locating pharmacies inside urban areas along with the high product perishability in this industry, the pharmaceutical supply chain management has recently gained increasing attention. Accordingly, this paper unveils an inventory-routing problem for designing a pharmaceutical supply chain with perishable products and time-dependent travel time in an uncertain environment.Design/methodology/approachIn this study, mathematical programming is employed to formulate a multi-graph network affected by the traffic volume in order to adapt to real-world situations. Likewise, by transforming the travel speed function to the travel time function using a step-by-step algorithm, the first-in-first-out property is warranted. Moreover, the Box–Jenkins forecasting method is employed to diminish the demand uncertainty.FindingsAn appealing result is that the delivery horizon constraint in the under-study multi-graph network may eventuate in selecting a longer path. Our analysis also indicates that the customers located in the busy places in the city are not predominantly visited in the initial and last delivery horizon, which are the rush times. Moreover, it is concluded that integrating disruption management, routing planning and inventory management in the studied network leads to a reduction of costs in the long term.Originality/valueApplying the time-dependent travel time with a heterogeneous fleet of vehicles on the multi-graph network, considering perishability in the products for reducing inventory costs, considering multiple trips of transfer fleet, considering disruption impacts on supply chain components and utilizing the Box–Jenkins method to reduce uncertainty are the contributions of the present study.

Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times

The Transmission Control Protocol (TCP) serves as a cornerstone mechanism for implementing Congestion Control (CC) across the Internet. Designing a solution that provides high bandwidth utilization and mitigates the phenomenon of bufferbloat across a spectrum of diverse scenarios poses a considerable challenge. The introduction of Bottleneck Bandwidth and Round Trip propagation time (BBR) in 2016 marked a significant shift in congestion control methodology. Its improved performance and adaptability contributed to the initial acclaim and widespread interest that it received.. Unlike most currently used CCs, it operates around Kleinrock’s optimal point, thus offering high throughput even in lossy networks while preventing buffer saturation. Unfortunately, it quickly became evident that BBR was unable to fairly share bandwidth with flows characterized by different path delays, as well as loss-based CCs. In response, Google recently introduced a third iteration to address these shortcomings. This study explores the performance of BBRv3 across a wide range of scenarios, thereby considering different buffer sizes and paths with varying Round Trip Times (RTTs), and it evaluates its superiority over its predecessors. Through extensive simulations, this work assesses whether BBRv3 can finally play fair with other bandwidth contenders, which is a critical consideration given the widespread deployment of BBR. The framework is publicly available to facilitate additional validation and ensure the reproducibility of the study’s findings. The results indicate that while BBRv3 demonstrates enhanced fairness towards loss-based CC algorithms, it struggles when competing against other BBR flows, especially in multi-RTT networks, thus falling short even when compared to the initial version.

An exact method for vehicle routing problem with backhaul discounts in urban express delivery network

The surge in e-commerce has led to an increased demand for urban express services, requiring the strategic development of delivery networks that are both efficient and cost-effective. This study addresses a practical vehicle routing problem (VRP) in an urban express delivery network to minimize transportation costs. Specifically, it considers the implementation of backhaul discounts, a factor disregarded in the existing literature. This VRP is further complicated by various realistic constraints, including pickup and delivery, time windows, multiple trips, heterogeneous fleets, and docking capacity limitations, which make most general VRP solvers inapplicable. This study proposes a trip-based formulation to overcome this challenge and develop a tailored branch-and-price algorithm. Feasible trips are classified into four types to simplify the computation of backhaul discounts, thereby enhancing solution efficiency. Validation with real-world data from SF Express substantiates the efficacy of our method and yields insights for sustainable city logistics management. Moreover, our simplified column generation algorithm exhibits competitive performance, achieving optimal solutions expeditiously for the tested instances.

Fast Commitment for Geo-Distributed Transactions via Decentralized Co-Coordinators

In a geo-distributed database, data shards and their respective replicas are deployed in distinct datacenters across multiple regions, enabling regional-level disaster recovery and the ability to serve global users locally. However, transaction processing in geo-distributed databases requires multiple cross-region communications, especially during the commit phase, which can significantly impact system performance. To optimize the performance of geo-distributed transactions, we propose Decentralized Two-phase Commit (D2PC), a new transaction commit protocol aiming to minimize the negative impact of cross-region communication. In D2PC, we employ multiple co-coordinators that perform commit coordination in parallel. Each co-coordinator is responsible for collecting 2PC votes and making a PreCommit decision in its local region. This approach allows for the concurrent invocation of multiple cross-region network round trips, and each region can end its concurrency control locally before replication is complete, thus significantly reducing the chances of blocking and enhancing system concurrency. Moreover, we propose the bypass leader replication reply method, leveraging decentralized co-coordinators to bypass the leader for message transmission, thereby reducing the commit latency. Experimental results have demonstrated that D2PC can reduce commit latency by 43% and improve throughput by up to 2.43 × compared to the geo-distributed transaction processing methods based on 2PC.

An efficient hybrid genetic algorithm for the traveling salesman problem with release dates

This paper deals with a generalization of the classic traveling salesman problem where each customer is associated with a release date, defined as the time at which the desired product becomes available at the depot. In this problem, a single uncapacitated vehicle is allowed to perform multiple trips in order to satisfy all demands. However, the vehicle cannot start a route unless all the products associated with the demands in the route are released. As a consequence, there might be a waiting time before starting the next route. The objective of the problem is to minimize the completion time of the service, defined as the time at which the vehicle returns to the depot after satisfying all demands. We propose a hybrid genetic algorithm that incorporates more advanced mechanisms to evaluate individuals and to ensure population diversity. We also introduce a novel dynamic programming splitting algorithm that converts a sequence of visits to customers, the so-called giant-tour, into the best set of routes that respects the sequence. Computational experiments performed on 522 benchmark instances show that our approach is able to find all 154 known optimal solutions from the literature. In addition, we were able to improve the best-known upper bounds for 364 instances in significantly shorter computational times when compared to the state-of-the-art.

An enhanced exact algorithm for the multi-trip vehicle routing problem with time windows and capacitated unloading station

This paper introduces a vehicle routing problem that simultaneously considers multiple trips, time windows, and a capacitated unloading station. This problem is a generalization of the multi-trip vehicle routing problem with time windows, which determines a set of least-cost vehicle routes to fulfill all customer demands while respecting the constraints of vehicle capacity and time windows. Due to restricted resources (e.g., equipment and labor force) at the depot, vehicles may need to wait in a queue for being unloaded when they arrive. This unloading capacity constraint significantly complicates the problem, as it causes a trip to involve three stages—traveling, waiting, and unloading. We formulate this problem as an arc flow model and a trip-based set partitioning model, where the latter is solved by a branch-price-and-cut (BPC) algorithm. To improve the computational aspect of the BPC framework, a two-phase column generation (CG) algorithm is designed. First, a bidirectional labeling algorithm is tailored to solve the pricing problem, where two accelerating strategies are employed to speed up the resolution process. Meanwhile, k-path inequalities and limited-memory subset row inequalities are utilized to tighten the linear relaxation of the master problem. Computational results based on the instances adapted from the well-known Solomon’s benchmark show that the developed BPC algorithm can solve most instances within 50 customers to optimality in a short time frame and some instances of 100 customers to optimality within a 3-hour time limit. Moreover, our BPC algorithm performs better than exact algorithms in the literature for similar problem variants in both solution quality and computing time.

An accelerated benders decomposition algorithm for the solution of the multi-trip time-dependent vehicle routing problem with time windows

The logistics companies executing the last mile delivery of goods in urban areas deal every day with the problem of routing their vehicles, while taking into account multiple trips per vehicle, time-dependent travel time, customers’ time windows and loading time at the depot simultaneously. This paper addresses this problem, known as Multi-Trip Time-Dependent Vehicle Routing Problem with Time Windows, aiming at its exact solution, minimizing the total travelled distance of a company's fleet. Based on a literature model, a new reformulation with reduced size is suggested. This reformulation is decomposed by applying the Benders method in an effective way, resulting in a subproblem with no duality gap. By exploiting the special features of the problem and the particular structure of the decomposition made, several novel valid inequalities are introduced, in order to both tighten the non-decomposed formulations and warm start the relaxed master problem to achieve less infeasible solutions and higher lower bounds. For the solution of the problem, an innovative algorithm is proposed, including suboptimal master solutions and a multi-cut generation procedure, which is based on the careful observation of the values of the Benders dual subproblem variables. The impact of the valid inequalities as well as two variants of the suggested algorithm are tested on benchmark data and they are compared with the non-decomposed models and a heuristic introduced in the literature. The computational results indicate improved efficiency and stronger bounds for the proposed algorithm.

Solidaristic Listening

Abstract Storytelling in solidaristic communities can foster agency and challenge oppression. However, power imbalances among community members can undermine that potential by contributing to the production of loneliness, where a person loses their sense of self and their sense of belonging within the solidaristic community. To prevent loneliness and to protect the liberatory potential of storytelling, we consider how what we call solidaristic listening might be supported. We first consider, but ultimately reject, empathy as a central feature of solidaristic listening. Despite its popularity in feminist philosophy, empathy is often unable to maintain the relational distance needed between conversation partners to appreciate their differences. Instead, we suggest an ongoing embodied, relational approach to foster solidaristic listening. We draw on three philosophical ideas to motivate our account: visiting the perspectives of others (Arendt 1992), visiting as a reciprocal exchange that requires multiple trips (Simpson 2017), and solidarity as traveling together (Medina 2013).

Individual agency in South-South policy transfer: China and Ethiopia’s industrial park development

This article process-traces Ethiopia’s adoption and implementation of the industrial park development strategy from the mid 2000s to the late 2010s. How does policy transfer unfold, and what characterizes China’s influence on Ethiopia’s Special Economic Zone (SEZ) strategy? Drawing on extensive interviews conducted during multiple field trips to Ethiopia and China between 2017 and 2023, complemented by secondary sources, we argue that policy transfer involves navigating both structural opportunities—providing incentives and resources for individuals—and individual agency to surmount structural constraints. Contributing to the growing body of literature on South-South policy transfer, our study underscores the pivotal role of individual agency in overcoming prevalent structural constraints within South-South contexts, characterized by fluid power relations and less defined policy transfer networks. Additionally, we emphasize the importance of scrutinizing the agency of host-country actors in their engagements with a rising China, rather than assuming China’s impacts in a deterministic manner. Our detailed documentation offers a nuanced perspective on China’s influence in Ethiopia’s industrialization. We show that primary agency in the policy transfer process should be attributed to Ethiopian actors, whereas the roles of Chinese actors range from initiation, facilitation, and occasionally, reluctant responses in this intricate process.

Non-linear effects of built environment factors on mode choice: A tour-based analysis

Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.

Heuristic Algorithms for Heterogeneous and Multi-Trip Electric Vehicle Routing Problem with Pickup and Delivery

In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches.

Optimalitas Rute pada Pengiriman Multiperjalanan dengan Armada Kendaraan Listrik Heterogen

Electric vehicles are emerging as a key trend in sustainable mobility, mitigating emissions, and reducing dependence on fossil fuels. The challenge in optimizing route modeling lies in some limitations, such as battery range, charging time, and the diversity of electric vehicle types. This article explores the optimality of routes in a multiple-trip distribution system using a heterogeneous fleet of electric vehicles. The electric vehicle routing problem is formulated as a mixed-integer linear programming aiming to find the most cost-efficient optimal route. A notable feature of the model allows electric vehicle fleets to undertake additional travel to complete distribution tasks, i.e., multiple trips. The model is implemented in two illustrative examples involving the delivery of goods using homogeneous and heterogeneous electric vehicle fleets characterized by loading and battery capacities. Each case includes one depot, 8 and 10 customers, and 2 battery swapping stations, solved using the branch-and-bound method through Lingo 18.0. Simulation results indicate that battery capacity and the presence of battery swapping stations significantly influence the routes selection.

Solving the Vehicle Routing Problem with Multiple Trips and Simultaneous Delivery-Pickup for Drinking Water Distribution Company

Transportation is a crucial factor in distribution, as it can save up to 50% of the overall costs. The Vehicle Routing Problem with Multiple Trips and Simultaneous Delivery-Pickup (VRPMTSDP) has become one of the most important areas of logistic management. This problem is an extension of the Vehicle Routing Problem (VRP), including the following characteristics: multiple trips and simultaneous delivery-pickup. A set of homogenous vehicles is available at the depot to deliver and pick up customer’s goods. The route of each vehicle in serving customers can cover more than one trip. This research helps to solve the problems faced by the Drinking Water Distribution Company in Bandung. Currently, they face high delivery costs and frequent driver overtime when returning to the depot, resulting in high overtime expenses. This problem can be classified as VRPMTSDP, with the aim of minimizing delivery costs. We propose a Tabu Search with the initial solution obtained using Saving Matrix and Nearest Neighbor. The results show that the proposed route by the Tabu Search algorithm saves delivery costs of 11.22% rather than the company’s current route. Sensitivity analyses are presented to understand the impact of various Tabu Search operators on the delivery cost of VRPMTSDP.