Transportation Research Part C-Emerging Technologies最新文献

{"title":"Optimising toll prices based on a dynamic multi-region MFD SUE traffic model: formulation and a case study of Zealand, Denmark","authors":"Lawrence Christopher Duncan ,&nbsp;Thomas Kjær Rasmussen ,&nbsp;David Paul Watling ,&nbsp;Ravi Seshadri","doi":"10.1016/j.trc.2025.105432","DOIUrl":"10.1016/j.trc.2025.105432","url":null,"abstract":"<div><div>Road use tolling is an effective way of alleviating congestion. Although many tolling models have been developed, there is gap in the research for a model that: i) is dynamic, ii) accounts for the impacts of tolls on travel demand and departure time choice, iii) accounts for stochasticity in travellers’ route choices, iv) is well-behaved, producing continuous outputs, and v) is computationally feasible to apply to real-life large-scale networks. This paper fills this gap, by developing a tolling model based on the dynamic multi-region Macroscopic Fundamental Diagram (MFD) Stochastic User Equilibrium (SUE) traffic model introduced in Duncan et al. (2025). We begin by extending the model to account for elastic demand and departure time choice. Then, we integrate the model within a toll-price optimisation framework, where the tolling scheme is travel-time-based and the objective function maximises social welfare. We first test the model in a small-scale example multi-region MFD system, and then apply it to estimate an optimal toll-price in a real-life large-scale and detailed case study of Zealand, Denmark. Experiments find that the model is well-behaved and produces smooth objective function surfaces with a unique maximum. Travel behaviour implications of tolling are also realistic, where some travellers opt not to travel by car, some change their departure time, and some change their route. Results suggest that tolling could instigate a positive change in travel behaviour to benefit society.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105432"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated optimization of ride-pooling and shared micro-mobility services with meeting points","authors":"Tingting Dong ,&nbsp;Yulong Hu ,&nbsp;Zemin Wang ,&nbsp;Sen Li","doi":"10.1016/j.trc.2025.105452","DOIUrl":"10.1016/j.trc.2025.105452","url":null,"abstract":"<div><div>This paper studies the integrated optimization of ride-pooling and micro-mobility services within a multimodal transportation network to enhance the likelihood of ride-pooling, reduce the waiting time for passengers, and improve the platform’s profit. We consider an integrated platform that simultaneously provides ride-pooling and micro-mobility services. Instead of door-to-door ride-pooling services, we consider inter-modal transfer at meeting points, where riders can access or egress nearby pickup or dropoff (PUDO) points using micro-mobility vehicles and then get picked up and dropped off at these meeting points by ride-pooling vehicles. We investigate the real-time operational strategies of such a multimodal system taking into account the availability of micro-mobility vehicles at meeting points. In view that riders’ PUDO choices are interdependent in the integrated services, we devise a new dual-graph-based method that enables the decomposition of ride-pooling vehicle routing decisions and assignment decisions. We develop several interconnected subproblems that consider the mutual impacts between ride-pooling and micro-mobility services while we jointly determine rider-vehicles assignment, PUDO selection, ride-pooling vehicle routing decisions, micro-mobility vehicles repositioning decisions, and the transportation of micro-mobility vehicles on ride-pooling vehicles along with riders. We devise efficient algorithms for constructing graphs, identifying feasible trips, and making routing decisions for ride-pooling vehicles based on dynamic programming. The proposed models and algorithms are validated using real-world ride-hailing and bike-sharing data from Manhattan, New York City. The tests demonstrate that our algorithms can efficiently compute optimal matching. The results suggest that jointly operating ride-pooling and micro-mobility services can enhance rider shareability and reduce the repositioning costs of micro-mobility vehicles, thus benefiting both systems. Compared to door-to-door services, the integrated services can increase the number of served riders by more than 10 % while reducing repositioning costs by more than 50 % in the morning peak hour.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105452"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of knowledge graph construction using large language models in transportation: Problems, methods, and challenges","authors":"Yancheng Ling ,&nbsp;Zhenlin Qin ,&nbsp;Zhenliang Ma","doi":"10.1016/j.trc.2025.105428","DOIUrl":"10.1016/j.trc.2025.105428","url":null,"abstract":"<div><div>In an era of unprecedented data availability and increasingly complex transportation systems, there is a pressing need for computational paradigms that can unify cross-disciplinary knowledge and systematically deduce new hypotheses. Knowledge graphs (KGs) provides a powerful approach in organizing and connecting fragmented evidence from multiple disciplines into a single, holistic analysis framework for deep scientific discoveries. The challenge is to automate the KG construction process by integrating diverse data sources and, importantly, harmonizing fragmented, incomplete, or even contradictory evidence that arises from multiple domains. Large language models (LLMs), trained in extensive corpora from multidisciplinary data, serve as a vast knowledge repository with advanced cognitive and reasoning capabilities. LLMs lends great opportunity to automate the KGs’ construction and expansion with transdisciplinary data integration and harmonization capabilities. To facilitate the quick adoption of KGs and LLMs in transportation, this paper presents a comprehensive review of LLMs for the construction of KGs with a particular focus on methodological development, including classifications, definitions, and challenges of KG construction tasks, and methodological pipelines and techniques using LLMs for these tasks. Building on these, we propose a LLM-driven pipeline for ontological transportation KG construction harmonizing un-/structured data across disciplines and generic purpose KGs. The graph evolves iteratively through an adaptive graph-refinement process, enabling updates with new findings and data while ensuring logical consistency and theoretical coherence. The transportation ontology system provides the structural backbone for the process, ensuring knowledge alignment to maintain semantic consistency across domains. Finally, we summarize the challenges from aspects of data quality, model capability, and computational costs, and outline future research directions. The study advances the use of LLMs for KG-based knowledge representation, facilitating automated discoveries and innovations in transportation.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105428"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing corner cases in autonomous driving: A world model-based approach with mixture of experts and LLMs","authors":"Haicheng Liao ,&nbsp;Bonan Wang ,&nbsp;Junxian Yang ,&nbsp;Chengyue Wang ,&nbsp;Zhengbing He ,&nbsp;Guohui Zhang ,&nbsp;Chengzhong Xu ,&nbsp;Zhenning Li","doi":"10.1016/j.trc.2025.105456","DOIUrl":"10.1016/j.trc.2025.105456","url":null,"abstract":"<div><div>Accurate and reliable motion forecasting is essential for the safe deployment of autonomous vehicles (AVs), particularly in rare but safety-critical scenarios known as corner cases. Existing models often underperform in these situations due to an over-representation of common scenes in training data and limited generalization capabilities. To address this limitation, we present WM-MoE, the first world model-based motion forecasting framework that unifies perception, temporal memory, and decision making to address the challenges of high-risk corner-case scenarios. The model constructs a compact scene representation that explains current observations, anticipates future dynamics, and evaluates the outcomes of potential actions. To enhance long-horizon reasoning, we leverage large language models (LLMs) and introduce a lightweight temporal tokenizer that maps agent trajectories and contextual cues into the LLM’s feature space without additional training, enriching temporal context and commonsense priors. Furthermore, a mixture-of-experts (MoE) is introduced to decompose complex corner cases into subproblems and allocate capacity across scenario types, and a router assigns scenes to specialized experts that infer agent intent and perform counterfactual rollouts. In addition, we introduce nuScenes-corner, a new benchmark that comprises four real-world corner-case scenarios for rigorous evaluation. Extensive experiments on four benchmark datasets (nuScenes, NGSIM, HighD, and MoCAD) showcase that WM-MoE consistently outperforms state-of-the-art (SOTA) baselines and remains robust under corner-case and data-missing conditions, indicating the promise of world model-based architectures for robust and generalizable motion forecasting in fully AVs.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105456"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesizing perceived trajectory for placing cooperative roadside LiDAR: A hybrid simulation-deep learning framework","authors":"Yang Ma ,&nbsp;Yubing Zheng ,&nbsp;Yang Liu ,&nbsp;Zhongxiang Feng ,&nbsp;Said M. Easa ,&nbsp;Yiik Diew Wong","doi":"10.1016/j.trc.2025.105473","DOIUrl":"10.1016/j.trc.2025.105473","url":null,"abstract":"<div><div>Although multiple roadside LiDARs (RSLs) units can work together, the RSL captured trajectory (RSL-CT) in real-world scenarios may deviate from the ground truth. These deviations can vary significantly depending on the placement of the RSLs. Therefore, it is crucial to virtually pre-estimate the perception performance of RSLs for any specific placement plan to maximize cost-effectiveness. Current studies for optimizing RSL placement primarily focus on evaluating performance in object detection rather than trajectory perception. However, trajectory perception is more important for various applications within cooperative vehicle-infrastructure systems. To address this gap, this paper introduces a hybrid cooperative perception simulation (CoP-SIM) framework that emulates RSL-based trajectory capturing. In CoP-SIM, three-dimensional models of real-world road environments are combined with real trajectory data to reconstruct digital-twin traffic scenarios. An improved ray-tracing algorithm is first applied to generate sensor data. Then, inspired by the neural radiance field (NeRF) model used in novel view synthesis, a deep neural network (DNN)-based method is proposed to synthesize RSL-CT for any new placement plan without the need for time-consuming simulations. Test results on the CitySim datasets demonstrate the feasibility of synthesizing RSL-CT using DNN. The Long short-term memory (LSTM) model outperforms the multi-layer perceptron (MLP) in synthesizing RSL-CT. This DNN-based RSL-CT can effectively pre-estimate RSL performance in risk monitoring. When compared to optimization methods that focus solely on enhancing object detection, using DNN-synthesized RSL-CT for optimizing RSL placement yields superior results, leading to improved accuracy in trajectory perception.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105473"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trustworthy vehicular trajectory prediction under observational attacks: an adaptive and generalized defense framework for deep learning models","authors":"Jialei Hu ,&nbsp;Geqi Qi ,&nbsp;Avishai (Avi) Ceder ,&nbsp;Dianchen Zhu","doi":"10.1016/j.trc.2025.105486","DOIUrl":"10.1016/j.trc.2025.105486","url":null,"abstract":"<div><div>Accurately and reliably predicting vehicle trajectories is critical for safe downstream decision-making. State-of-the-art Deep Learning (DL) models typically rely on historical trajectories recorded by onboard sensors. However, the common assumption that transmitted trajectories match ground truth is often violated due to observational attacks, which mislead DL models with corrupted inputs. Existing Training-Augmentation (TA) approaches attempt to improve robustness by injecting attacked samples into training, but they suffer from confidence issues and poor generalizability across diverse attacks due to their stochastic nature and causal confusion. To address these limitations, we propose an adaptive, generalized defense framework compatible with any DL model and trained only on normal (attack-free) data. The core idea is intuitive: output multimodal predictions under high uncertainty and otherwise follow a deterministic trajectory. We quantify epistemic uncertainty induced by observational attacks using a Bayesian Neural Network with entropy decomposition, where epistemic uncertainty reflects a model’s knowledge boundary and sensitivity to rare or unseen cases. Our defense employs a two-phase trajectory generation mechanism. Phase 1 estimates epistemic uncertainty and determines whether Phase 2 is triggered. If uncertainty exceeds a predefined threshold, Phase 2 generates multimodal trajectories by updating distributional parameters using both learned knowledge and training-independent physical priors, improving adaptability to dynamic attacks. Experiments demonstrate strong generalizability across multiple DL models and attack types. Ablation studies highlight the contributions of each component and parameter setting. Finally, case studies visualize scenarios where only Phase 1 or both phases are activated, providing further insight into the defense behavior.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105486"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards full-scenario safety evaluation of automated vehicles: A volume-based method","authors":"Hang Zhou ,&nbsp;Chengyuan Ma ,&nbsp;Shiyu Shen ,&nbsp;Zhaohui Liang ,&nbsp;Xiaopeng Li","doi":"10.1016/j.trc.2025.105485","DOIUrl":"10.1016/j.trc.2025.105485","url":null,"abstract":"<div><div>With the rapid development of automated vehicles (AVs) in recent years, commercially available AVs are increasingly demonstrating high-level automation capabilities. However, most existing AV safety evaluation methods are primarily designed for simple maneuvers such as car-following and lane-changing. While suitable for basic tests, these methods are insufficient for assessing high-level automation functions deployed in more complex environments. First, these methods typically use crash rate as the evaluation metric, whose accuracy heavily depends on the quality and completeness of naturalistic driving environment data used to estimate scenario probabilities. Such data is often difficult and expensive to collect. Second, when applied to diverse scenarios, these methods suffer from the curse of dimensionality, making large-scale evaluation computationally intractable. To address these challenges, this paper proposes a novel framework that is designed for the safety ranking of AVs and has the potential to be extended to full-scenario applications. A unified model is first introduced to standardize the representation of diverse driving scenarios. This modeling approach constrains the dimension of most scenarios to a regular highway setting with three lanes and six surrounding background vehicles, significantly reducing dimensionality. To further avoid the limitations of the probability-based method, we propose a volume-based evaluation method that quantifies the proportion of risky scenarios within the entire scenario space. For car-following and lane-changing scenarios, we prove that the set of safe scenarios has convex properties under specific settings, enabling exact volume computation. Experimental results validate the effectiveness of the proposed volume-based method in representative scenarios, including car-following and lane-changing, using both AV behavior models from existing literature and six production AV models calibrated from field-test trajectory data in the Ultra-AV dataset.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105485"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driver cognitive load estimation in conditional driving with aligned attention-enabled multimodal fusion","authors":"Ange Wang ,&nbsp;Haohan Yang ,&nbsp;Jiyao Wang ,&nbsp;Hai Yang ,&nbsp;Dengbo He","doi":"10.1016/j.trc.2025.105471","DOIUrl":"10.1016/j.trc.2025.105471","url":null,"abstract":"<div><div>Despite the promise of autonomous driving in enhancing road safety, drivers in conditionally automated vehicles are still responsible for driving safety and thus driver state still matters to driving safety. Though driving automation can reduce taskload of drivers, they may still experience high cognitive load, which can impair takeover performance. However, existing cognitive load estimation algorithms were primarily designed for non-automated vehicles, which may not be applicable in conditionally automated vehicles, due to the differences in driver responsibilities and the availability of certain metrics (e.g., driving performance measures are absent when drivers are not controlling the vehicle). Further, existing driver cognitive load algorithms rarely considered the integration of both spatial and temporal information in the input features. Therefore, we proposed an aligned-attention transformer network that integrates the multi-stream transformer network with alignment attention to estimate the cognitive load of drivers in conditionally automated vehicles. The algorithm fuses physiological measures that can potentially be measured non-intrusively in vehicles, i.e., electrocardiogram, electrodermal activity, and respiration signals. To validate the efficacy of the algorithm, we supplement a European dataset with a self-collected Chinese dataset, in which 42 drivers engaged in various cognitive tasks (i.e., memory, calculation, and spatial tasks). The results showed that our algorithm outperformed state-of-the-art driver cognitive estimation algorithms on both within-subject and across-subjects data partitions. Further, ablation tests validated the robustness of our algorithm and the effectiveness of the network modules. This research can guide the design of driver state monitoring systems in both non-automated vehicles and conditionally automated vehicles.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105471"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A meta-learning enhanced deep reinforcement learning approach for generalizing across orienteering problem with time windows","authors":"Hyunjoon Kim,&nbsp;Stephane Barde","doi":"10.1016/j.trc.2025.105450","DOIUrl":"10.1016/j.trc.2025.105450","url":null,"abstract":"<div><div>The Orienteering Problem with Time Windows (OPTW) is a complex combinatorial optimization problem with applications in logistics, tourist route planning, and emergency services. Traditional methods for solving OPTW, including metaheuristics, often struggle with scalability, adaptability, and generalization to new instances. Recently, deep reinforcement learning (DRL) has shown promise in tackling routing problems. However, existing DRL methods typically rely on non-Markovian state representations and handcrafted masking rules, which limit their adaptability and generalization. This paper presents Meta Pointer Network for OPTW (MetaPNet-OPTW), a meta-learning-enhanced DRL framework that combines a Markovian state formulation with OR-based feasibility rules within a pointer network model. We introduce the Meta-Learning enhanced REINFORCE algorithm, which learns across diverse problem instances and enables rapid adaptation to unseen configurations with minimal fine-tuning. During inference, active search with beam search is used to refine solutions dynamically. Extensive experiments show that MetaPNet-OPTW outperforms existing DRL approaches in efficiency and generalization, and notably improves 20 of 33 best-known solutions on the <em>Gavalas</em> benchmark. We further provide a t-SNE analysis of the learned latent space, enriched with spatio-temporal statistics, which explains why the model excels on <em>Gavalas</em> instances while identifying harder clusters such as <em>r2</em> and <em>c2</em>. This study contributes a scalable DRL framework for OPTW that not only achieves state-of-the-art performance but also provides new interpretability into benchmark difficulty and model adaptability.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105450"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the benefits of collaborative ridesharing across transportation network companies","authors":"Xiaohan Wang ,&nbsp;Zhan Zhao ,&nbsp;Hongmou Zhang ,&nbsp;Xiaotong Guo ,&nbsp;Jinhua Zhao","doi":"10.1016/j.trc.2025.105475","DOIUrl":"10.1016/j.trc.2025.105475","url":null,"abstract":"<div><div>By allowing two or more passengers to dynamically share parts of their trips in a single vehicle, ridesharing can lead to significant vehicle mileage traveled (VMT) savings, but its potential is often limited by market fragmentation, due to the co-existence of multiple Transportation Network Companies (TNCs). Although a collaborative ridesharing market that allows sharing across TNCs can produce additional VMT savings, to what extent such benefits vary based on market characteristics and behaviors of individual TNCs remains insufficiently understood. This study presents a framework to assess the maximum potential benefits of collaborative ridesharing and the contrasting equilibrium benefits resulting from varying strategic behaviors of TNCs. Specifically, we adopt a multi-TNC shareability network approach to estimate the maximum benefits under various market conditions, assuming that all TNCs are fully collaborative. In reality, TNCs’ willingness to collaborate is primarily motivated by their own profit gains, and thus we further propose a game-theoretic model to capture the collaboration dynamics among TNCs as a Nash game, allowing us to estimate the equilibrium benefits of collaborative ridesharing and evaluate the effectiveness of various profit-sharing schemes. Using the real-world TNC market in Manhattan, New York City as a case study, we find that a fully collaborative ridesharing market can generate additional VMT savings of up to 10.3 % over the existing fragmented market, and this upper bound is jointly determined by demand density, market division, competition intensity, and trip length. The Nash game results also reveal that the TNCs’ willingness to collaborate largely depends on the profit-sharing scheme; the Shapley value scheme tends to favor smaller, higher-priced TNCs, while the Equal Profit Method benefits dominant TNCs and more effectively facilitates collaboration among TNCs with greater pricing disparities. The proposed framework provides valuable insights for market regulators and business alliances, enabling them to evaluate collaboration outcomes and design appropriate profit-sharing schemes to promote and sustain collaborative ridesharing.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105475"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}