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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Real-time order dispatch for on-demand food-delivery platforms with a mixed fleet of drones and couriers</title>
      <link>https://trid.trb.org/View/2686786</link>
      <description><![CDATA[This paper investigates a dynamic, multimodal delivery system where a platform coordinates a mixed fleet of drones and human couriers to provide real-time delivery services. Launchpads serve as hubs, enabling order transferring between couriers and drones. Drones operate exclusively between launchpads, while couriers handle first- and last-mile deliveries with order bundling, either directly completing orders or delivering them to or from launchpads. The platform determines order delivery mode, assigns couriers to orders, and repositions idle drones to minimize total operational costs while meeting order delivery deadlines. We formulate this as a mixed-integer linear program (MILP), capturing seamless coordination among couriers and drones under a re-optimization policy. A novel decomposition method integrating graph-based methods with mathematical programming is proposed to address the computational complexity. This method models matches between couriers and order delivery legs via a hypergraph and drone movements via a time-space network. These components are integrated into a master trip assignment problem, which is solved iteratively using a column-and-row generation framework where new hyper-edges prescribing many-to-many courier-order matches are identified by solving smaller MILPs. Numerical experiments using real-world food delivery data demonstrate the solution quality and efficiency of the proposed method. The simulation results reveal that the coordinated courier-drone delivery services achieve about 8% improvements in both service rates and ground operational cost reduction compared to traditional door-to-door delivery models.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686786</guid>
    </item>
    <item>
      <title>A three-sided network equilibrium model for on-demand food delivery services</title>
      <link>https://trid.trb.org/View/2689859</link>
      <description><![CDATA[The on-demand food delivery (OFD) industry has experienced significant growth in recent years; however, this rapid expansion has also presented numerous operational challenges for OFD platforms. While existing studies on the operational design for OFD platforms offer valuable managerial insights, few of them have considered spatial heterogeneity which greatly impacts the OFD market because many drivers deliver orders as bundles, i.e., delivering multiple orders in a trip. This work develops a network equilibrium model to capture the complex interactions among the market’s three major players, namely, customers, drivers, and merchants. We consider a game-theoretical framework in the Stackelberg leader-follower structure. As the leader, the OFD platform aims to achieve its desired objectives by leveraging two major operations: (1) the batch-matching between delivery drivers and orders, and (2) the bundling delivery dispatching which optimizes the drivers’ routes in delivering multiple orders per ride. Three market players are regarded as followers, with their behaviors depicted by utility-based discrete choice models, and their interactions on a network scope captured by the three-sided network equilibrium model. We formulate the matching and delivering problem as a mathematical program with equilibrium constraints, and develop a coordinate descent–based algorithm to solve it efficiently. Through extensive numerical studies on real-world data, we showcase the efficacy of our proposed model in evaluating the performance of various operational strategies for OFD platforms. Our analysis offers insights into the impacts of platform operations on market players from a stationary equilibrium perspective. The proposed model can be utilized as an analytical tool to assist OFD platforms and the government in high-level planning that enhances efficiency and sustainability.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689859</guid>
    </item>
    <item>
      <title>A Sageformer-KAN deep learning model for demand prediction of online food deliveries</title>
      <link>https://trid.trb.org/View/2685531</link>
      <description><![CDATA[The rapid growth of online food delivery (OFD) demand, fueled by recent consumer habit changes and technological advancements, has called for more efficient instant logistics resource management. Accurately forecasting the spatiotemporal distribution of orders to support real-time resource allocation has become an urgent challenge facing the OFD platforms. This study introduces Sageformer-Kolmogorov-Arnold network (KAN), a deep learning model for predicting food delivery demand using merchant-customer origin-destination (OD) pairs. With a multi-hop graph neural network for spatial dependencies, a transformer encoding block for temporal dependencies, and a KAN forecasting head for predictions, the model was tested on real-world data, outperforming baseline models in demand forecasting. It demonstrates excellent performance across different temporal and spatial scenarios. This study contributes to estimating the spatiotemporal order structure by forecasting future order distributions. The model aids in guiding couriers’ real-time locations, thus improving the balance between supply and demand in the OFD industry.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:52:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685531</guid>
    </item>
    <item>
      <title>Optimization of Elderly Meal Delivery Routes Considering Heterogeneity</title>
      <link>https://trid.trb.org/View/2711996</link>
      <description><![CDATA[With the intensification of aging, smart elderly care increasingly plays an irreplaceable role in alleviating the pressure of elderly care. Providing meal delivery services for the elderly with the help of smart tools could help solve the dining problems of the elderly. Considering heterogeneity within the elderly population, this paper considers those who have meals at home and those who choose to eat at community service centers—the two dining locations encompassing different time windows. This study investigated the path optimization problem of the joint delivery of elderly meals by trucks and robots. At the same time, it considered the battery capacity of the robots and constructed a mixed-integer programming model to minimize the total cost of the system. For large-scale problems, an improved adaptive large neighborhood search algorithm was used, which integrated the ant colony algorithm to solve them. The investigation confirmed the feasibility and effectiveness of the algorithm. Based on the research results, this paper offers management and key stakeholders practical guidance for implementing elderly meal delivery.]]></description>
      <pubDate>Tue, 09 Jun 2026 10:54:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711996</guid>
    </item>
    <item>
      <title>Routing Optimization of Takeout Delivery Routes Under Joint Delivery Model of Drones, Occasional Drivers, and Riders</title>
      <link>https://trid.trb.org/View/2658975</link>
      <description><![CDATA[Takeout delivery route optimization is a challenging research topic in the field of e-commerce. In this paper, aiming at the problems such as limited service range, unreasonable allocation, and tight time windows in the takeout delivery process, we propose a collaborative delivery model with multi-distribution center cooperation, where both drones and occasional drivers collaborate with riders. That is, on the basis of using the joint delivery of drones and riders for long-distance orders, occasional drivers are used to deliver long-distance and some orders within the range, giving play to the advantages of the crowdsourcing model and complementing the drone delivery model to further utilize the advantages of both delivery methods. With the minimum delivery cost and the overall maximum customer satisfaction as the objective function, a model is constructed. In this paper, an improved adaptive large neighborhood search algorithm (IALNS) is designed to solve it. The affinity propagation (AP) clustering is adopted to generate the initial solution, combined with multiple destruction operators and repair operators. Meanwhile, the tabu search framework is embedded locally to optimize the sub-solutions, and the simulated annealing framework is embedded to expand the global search range. The experimental results show that this algorithm effectively improves the solution quality and efficiency. At the same time, compared with the rider-only delivery mode, it has been proven that this model can further reduce the delivery cost and improve customer satisfaction. Finally, sensitivity analysis further demonstrates the advantages of the crowdsourcing model.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658975</guid>
    </item>
    <item>
      <title>On-demand restaurant meal delivery with synchronized multi-orders</title>
      <link>https://trid.trb.org/View/2661779</link>
      <description><![CDATA[On-demand restaurant meal delivery platforms, such as DoorDash and Meituan, have recently introduced a multi-order delivery service: Customers may combine delivery requests from different restaurants in a single multi-order with the service promise of a synchronized delivery. However, the platform must not only ensure the synchronization of multi-orders but also improve punctuality and freshness for all customers. This is challenging because, as we show, synchronization, delay, and freshness are conflicting objectives. Uncertainty in the delivery process and unknown future orders further complicate the decision making. This raises several research questions: How does the introduction of a multi-order service affect the overall delivery operations with regard to service quality and operational expenses? How should a multi-order service be strategically rolled out? How can we balance the competing objectives of synchronizing deliveries while minimizing delay and maximizing freshness? To answer the research questions, we propose an effective policy that allows for a careful and controlled balance between the competing objectives and employ it in an extensive computational study. We evaluate the effect of different trade-offs between delay, freshness, and synchronization on delivery operations over varying demand for multi-orders. We observe that enforcing strict synchronization of multi-orders by assigning each multi-order to a single delivery driver is hardly operational feasible. Occasionally using split-deliveries provides the flexibility to better balance all objectives. Our detailed experiments further generate insights on how platforms may roll-out multi-orders as a new service offering without negatively affecting their existing delivery operation while benefiting from reduced operational expenses.]]></description>
      <pubDate>Thu, 30 Apr 2026 09:11:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661779</guid>
    </item>
    <item>
      <title>What Factors Influence Risky Riding Behaviors Among Food Delivery Riders in Malaysia?</title>
      <link>https://trid.trb.org/View/2579899</link>
      <description><![CDATA[This study examined the factors that influence risky-riding behaviours among riders who worked in the food delivery services in Klang Valley. The expanded theory of planned behaviour was utilised as the foundation, and this study introduced two new constructs: conformity tendency and traffic environment to suit the study context. This study employed 211 food delivery riders to test the hypotheses, and a structural equation modelling approach based on the parameter estimation of partial least squares (PLS-SEM) method was used to evaluate the data. The PLS-SEM model was employed to estimate the proposed model. Based on the PLS-SEM estimation, the proposed model can explain about 67.6% and 69.0% of the factors that lead to food delivery riders’ behaviour intention and risk riding behaviour. The PLS-SEM results showed that the attitude, subjective norms, perceived behavioural control, and traffic environment have a statistically significant influence on the intention of food delivery riders related to risk riding. In addition, the behaviour intention has significant effects on risk-riding behaviour among food delivery riders in Klang Valley. Also, another analysis, a multi-group analysis, was carried out to find out any discrepancies in patterns between riders who were below 30 years old and their counterpart of riders above 30 years old. Based on these findings, a strategy that sought to lower risky riding behaviours and accidents among food delivery riders in Malaysia can be outlined.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579899</guid>
    </item>
    <item>
      <title>Assessing wholesale market relocation decisions considering mobile vendor routing: A generalisable GIS-based spatial analytics model</title>
      <link>https://trid.trb.org/View/2692429</link>
      <description><![CDATA[Urban wholesale market relocations often impose additional travel burdens on mobile vendors operating at low margins in the informal sector and create spatial inequities when guided by non-spatial criteria. This study, aiming at assessing the impact of market relocations on mobile vendors, presents a novel GIS-based framework that combines the Clarke and Wright Savings Algorithm for route optimisation with Kernel Density Estimation, a machine learning technique, to identify high intensity vendor corridors. Heatmap values of vendor activity and average distance metrics were normalised and candidate market sites were ranked using a new Equity-Efficiency Index measure which accounts for landuse policy preferences. Candidate sites were mapped further considering gain/loss in accessibility, for classifying the service area into improvement and degradation zones weighted by population. A case study based on the relocation of Segiri market in Samarinda, Indonesia has been developed to illustrate the method. Results show that there is a new location in Samarinda that can deliver large efficiency gains in key corridors but resulting in widespread service area losses. Other potential locations achieve moderate, evenly distributed accessibility improvements. The framework delivers spatially explicit evidence of benefit and burden distribution and supports relocation decisions that balance logistical efficiency, equitable access and urban resilience.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692429</guid>
    </item>
    <item>
      <title>A causal discovery and inference framework for on-demand food delivery delays</title>
      <link>https://trid.trb.org/View/2685555</link>
      <description><![CDATA[The on-demand food delivery (OFD) industry has seen growth yet grapples with arrival delays. For major platforms like DoorDash and Uber Eats, over one-third of orders arrive late, highlighting the severity of this challenge. Currently, few studies distinguish true drivers of these delays from mere correlations. This study addresses this gap by developing an innovative framework combining Bayesian causal discovery with double machine learning. From 405,180 OFD records in China, we found that 16.7% of orders experienced delays. Pickup and transport durations exhibited the strongest causal effects to these delays. In addition, delay propagation was first identified within OFD services, where delays in preceding orders significantly increase the length of subsequent delays. The findings offer practical insights for OFD platforms to reduce order delays, such as optimizing courier pickup processes and mitigating delay propagation. By targeting these root causes, platforms can enhance operational efficiency and make their services more sustainable.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:30:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685555</guid>
    </item>
    <item>
      <title>Uncovering spatiotemporal coupling of electricity outages and food access disruptions during disasters</title>
      <link>https://trid.trb.org/View/2689450</link>
      <description><![CDATA[Disaster-induced power outages create cascading disruptions across urban lifelines, yet the timed coupling between grid failures and essential service access remains poorly understood. Focusing on Hurricane Beryl in Houston (2024), this study integrates 173,000 15-minute outage records with 1.25 million revealed visits to 3,187 food facilities across 140 ZIP Code Tabulation Areas to quantify how infrastructure performance and human access co-evolve. We construct daily outage and food-access indices, estimate cross-system lags using lagged correlations, and identify recovery patterns with Dynamic Time Warping k-means clustering. Results reveal a consistent two-day lag: food access reaches its nadir on July 8, while outage severity peaks on July 10, with correlations strongest at a two-day delay. Overlaying outage and access clusters produces four compound typologies, showing road network sparsity drives persistent access loss. We identify 294 critical food facilities requiring targeted continuity measures. The framework offers a generalizable template for diagnosing cascading disruptions and informing recovery prioritization.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:37:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689450</guid>
    </item>
    <item>
      <title>Optimizing order bundling and dispatching in online food delivery for enhanced delivery efficiency</title>
      <link>https://trid.trb.org/View/2652354</link>
      <description><![CDATA[The online food delivery industry is undergoing a rapid global expansion, making it an easily accessible service for a growing number of consumers. With a simple swipe on a smartphone, customers can conveniently order food from a wide range of restaurants through online food delivery platforms such as Uber Eats, Grubhub, Meituan, and Eleme. The core functionality of these platforms is their algorithmic approach to order dispatching, which is the focus of our study. The objective is to optimize the courier-order matching process, thereby minimizing delivery time and distance, enhancing the efficiency and effectiveness of the service. In contrast to the conventional approach of matching a single order to each courier, our study explores a one-to-many courier-order matching process, which we term a concurrent order dispatching process optimizing order bundling, courier matching and route planning jointly. Our study proposes a comprehensive framework that intricately models the concurrent order dispatching process in great detail. Specifically, we construct a mixed-integer programming model and develop a hybrid heuristic algorithm to address the issue in an efficient manner. We introduce a novel order-bundling closeness measurement value to strategically dispatch multiple orders concurrently to each single courier during a designated decision time window. To assess the model’s and algorithm’s efficacy, we conducted experiments on both small-scale synthetic instances and large-scale real cases, utilizing data from a prominent online food delivery platform in China. The computational results demonstrate that our proposed approach yields solutions that are very close to the optimum in small-scale cases, and achieves significant improvements in terms of average delay time reduction and average distance savings in large-scale cases. In particular, our approach can save the average distance per order by 1.8 km and reduce the average delay time per order from 35 min to 10 min, in comparison to existing policies. We seek to ensure that a significant portion of orders are delivered on time, even with a limited number of couriers.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:35:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652354</guid>
    </item>
    <item>
      <title>A Prior and Posterior Order Postponement Framework for the On-Demand Food Delivery Problem</title>
      <link>https://trid.trb.org/View/2617689</link>
      <description><![CDATA[The rapid expansion of the on-demand food delivery (OFD) market has led the service providers to manage large-scale dynamic order dispatching. Postponing the dispatch of orders is an effective strategy to alleviate the pressures from sudden order surges and to enhance decision-making quality. This paper addresses the OFD problem with order postponement to minimize travel distances and delayed deliveries, focusing on deciding which orders to postpone and which rider to assign for each remaining order at each decision point. We propose a prior and posterior postponement framework that separates the postponement decision-making process into two phases to balance computational efficiency and decision quality. In the prior phase, multiple knowledge-based postponement rules are designed to quickly filter out orders unsuitable for immediate dispatch. In the posterior phase, a data-driven postponement strategy using reinforcement learning is developed to further optimize long-term objectives. Particularly, an action-oriented phase-specific reward shaping method is designed by analyzing the intrinsic nature of the order postponement process, which helps customize the postponement duration for each order to achieve better postponement performance. Extensive numerical ablation and comparative experiments using real-world data demonstrate that the proposed postponement approach is able to improve customer satisfaction, delivery efficiency, and rider experience better than existing methods. Managerial insights are provided regarding the value of order postponement, key factors for designing effective postponement strategies, and practical ready-to-use postponement tactics.]]></description>
      <pubDate>Tue, 24 Mar 2026 17:01:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617689</guid>
    </item>
    <item>
      <title>Transportation SuperApps and Urban Foodscapes: A Comparative Spatial Analysis of Online Food Merchant Distribution in Jakarta, Bandung, and Medan</title>
      <link>https://trid.trb.org/View/2674229</link>
      <description><![CDATA[The rapid expansion of Transport Super Applications (TSAs) has reshaped urban mobility and facility distribution. However, research on how TSAs influence facility distribution, particularly Online Food Merchants (OFMs), remains limited. This study examines the spatial clustering of OFMs in Jakarta, Bandung, and Medan using the Agglomeration Index, comparing them with public facilities as contrafactual. Data was collected through Google Maps scraping and analyzed to measure agglomeration patterns across neighborhoods. Results show significant variations: Jakarta exhibits strong clustering in commercial zones, Bandung has a more balanced distribution, and Medan displays mixed agglomeration levels. The findings highlight the role of digital platforms in shaping urban service ecosystems, affecting travel behavior and economic activities. Understanding these patterns is crucial for urban planning and policy-making to optimize facility distribution and mitigate congestion. This study contributes to discussions on digital disruption, urban agglomeration, and smart city development.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674229</guid>
    </item>
    <item>
      <title>Mobile Grocery Store Shopping and Its Impact on Social Interaction</title>
      <link>https://trid.trb.org/View/2655499</link>
      <description><![CDATA[In recent years, the problem of food deserts has been becoming more serious, especially in rural areas of Japan. Accordingly, the Japanese Ministry of Economy, Trade and Industry (METI) published the “Shopping Accessibility Aid Manual” to showcase forward-thinking initiatives (e.g., mobile grocery stores). However, there are no appropriate statistical models that can evaluate the demand for shopping support services and their social benefits. Consequently, it may be difficult for local governments to evaluate the need for shopping support services from the perspectives of both demand and social benefits. This study focused on mobile grocery stores as one of the shopping support services and aimed to clarify the factors that influence (1) the frequency of mobile grocery store shopping and (2) the impact of mobile grocery store shopping on social interaction.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655499</guid>
    </item>
    <item>
      <title>The role of gig worker earnings and workload in the adoption of bicycles in last-mile freight: A case of hyper-local food delivery in India</title>
      <link>https://trid.trb.org/View/2647774</link>
      <description><![CDATA[The use of motorized two-wheelers (MTW), for one-to-one food delivery trips is generating negative externalities. Though the use of alternative vehicular technologies like electric vehicles and delivery robots is effective in mitigating the externalities, bicycles remain the most feasible solution in the short term. However, worker earnings and workload remain the chief barriers to the large-scale adoption of bicycles. Food delivery workers are gig workers, being paid per delivery. The use of a bicycle, instead of an MTW, lowers the number of orders a delivery worker can fulfill, and thus curtails their earnings. Moreover, human-powered forms of transport like bicycles are limited in terms of the total distance they can travel in a day, necessitating a limit in terms of workload. In this context, this study evaluates three scenarios of hyper-local food delivery. In the first scenario, only MTWs are used for delivery. In the second scenario, bicycles are used for delivery distances below a threshold of 1.5km, and MTWs are used for the rest. In the third scenario, this threshold distance is increased to 2.5km. A two-step agent-based simulation is proposed, which is parameterized using data collected from delivery workers in a hyper-local zone in the city of Kolkata, India. Results from the operational side reveal that the partial replacement of MTW trips by bicycles can increase delivery time by 2.93-4.88 minutes, and lower emissions by 3.5-17.6 %. However, bicycle use can also lower gig worker earnings, necessitating the need for policies that can augment their earnings.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647774</guid>
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