@article{BEOJONE2024104717,

title = {A hierarchical control framework for vehicle repositioning in ride-hailing systems},

author = {Caio Vitor Beojone and Pengbo Zhu and Isik Ilber Sirmatel and Nikolas Geroliminis},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24002389},

doi = {https://doi.org/10.1016/j.trc.2024.104717},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104717},

abstract = {This paper introduces a multi-layer control strategy for efficiently repositioning empty ride-hailing vehicles, aiming to bridge the gap between proactive repositioning strategies and micro-management. The proposed framework consists of three layers: an upper-layer employing an aggregated model based on the Macroscopic Fundamental Diagram (MFD) and model predictive control (MPC) to determine optimal vehicle repositioning flows between each pair of regions, a middle-layer converting macroscopic decisions into dispatching commands for individual vehicles, and a lower-layer utilizing a coverage control algorithm for demand-aligned positioning guidance within regions. The upper-layer contributes to the proposed framework by providing a global (macroscopic) view and predictive capabilities including traffic and congestion features. The middle-layer contributes by ensuring and optimal assignment of repositioning vehicles, considering the decision from the upper- and lower- layers. Finally, the lower-layer contributes with operational details at the intersection or node level providing the precision required for microscopic vehicle guidance. Experimental validation using an agent-based simulator on a real network in Shenzhen confirms the effectiveness and efficiency of the framework in improving empty vehicle repositioning strategies for ride-hailing services in terms of average passenger waiting time and abandonment rates.},

keywords = {Coverage control, Hierarchical control, Macroscopic fundamental diagram (MFD), Model predictive control (MPC), TR-Part_C-SI, Vehicle repositioning},

pubstate = {published},

tppubtype = {article}

}

@article{CHENG2024104746,

title = {An Autonomous Modular Public Transit service},

author = {Xi Cheng and Yu (Marco) Nie and Jane Lin},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24002675},

doi = {https://doi.org/10.1016/j.trc.2024.104746},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104746},

abstract = {In this work, we present a proof-of-concept investigation of Autonomous Modular Public Transit (AMPT) at a network scale and compare it with the traditional fixed-route, fixed-vehicle size transit service in terms of total cost, which consists of both agency’s capital and operational cost (including energy cost) and passenger time cost. We formulate and solve stylized design models for AMPT on a grid network in a range of demand density scenarios with both homogenous and heterogeneous distributions. The AMPT models explicitly account for pod joining and disjoining (and therefore en-route transfers of passengers) and potential energy savings due to pod train formation (pod platooning), which represent major departures from the traditional transit models in the literature. Numerical results find that AMPT, if designed properly, may save the total cost compared to traditional transit systems thanks to demand responsive pod train capacity, particularly in the low demand scenarios. The cost savings of AMPT are largely attributed to passenger time saving by en-route transfer; the agency cost of AMPT has a mixed picture. The load factor of AMPT generally improves over the traditional transit service. We also show how key parameter values may affect the AMPT costs through sensitivity analysis.},

keywords = {Agency and passenger costs, Autonomous Modular Public Transit (AMPT), Autonomous Modular Vehicle Technology (AMVT), Gridded Fixed-route Transit Network, Joining/Disjoining of Pods, Pod, TR-Part_C-SI},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2024104719,

title = {Calibrating car-following models via Bayesian dynamic regression},

author = {Chengyuan Zhang and Wenshuo Wang and Lijun Sun},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24002407},

doi = {https://doi.org/10.1016/j.trc.2024.104719},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104719},

abstract = {Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-following models prioritize computational efficiency and interpretability by using a parsimonious nonlinear function based on immediate preceding state observations. However, this approach disregards historical information, limiting its ability to explain real-world driving data. Consequently, serially correlated residuals are commonly observed when calibrating these models with actual trajectory data, hindering their ability to capture complex and stochastic phenomena. To address this limitation, we propose a dynamic regression framework incorporating time series models, such as autoregressive processes, to capture error dynamics. This statistically rigorous calibration outperforms the simple assumption of independent errors and enables more accurate simulation and prediction by leveraging higher-order historical information. We validate the effectiveness of our framework using HighD and OpenACC data, demonstrating improved probabilistic simulations. In summary, our framework preserves the parsimonious nature of traditional car-following models while offering enhanced probabilistic simulations. The code of this work is available at https://github.com/Chengyuan-Zhang/IDM_Bayesian_Calibration.},

keywords = {Bayesian inference, Car-following models, Dynamic regression, Microscopic traffic simulation, TR-Part_C-SI},

pubstate = {published},

tppubtype = {article}

}

@article{WANG2024104577,

title = {Data poisoning attacks on traffic state estimation and prediction},

author = {Feilong Wang and Xin Wang and Yuan Hong and R. Tyrrell Rockafellar and Xuegang (Jeff) Ban},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24000986},

doi = {https://doi.org/10.1016/j.trc.2024.104577},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104577},

abstract = {Data has become ubiquitous nowadays in transportation, including vehicular data and infrastructure-generated data. The growing reliance on data poses potential cybersecurity issues to transportation systems, among which the so-called “data poisoning” attacks by adversaries are becoming increasingly critical. Such attacks aim to compromise a system’s performance by adding systematic and malicious noises, perturbations, or deviations to the dataset used by the system. Formal investigations of data poisoning attacks are essential for understanding the attacks and developing effective defense methods. This study develops a general data poisoning attack model for traffic state estimation and prediction (TSEP) that is a basic application in transportation. We first formulate data poisoning attacks as a general sensitivity analysis of parameterized optimization problems over parameter changes (i.e., data perturbations) and study the Lipschitz continuity property of the solution with the presence of general (equality and inequality) constraints. Then, we develop attack models that fit a broader spectrum of learning applications (such as TSEP) by extending existing models that only focus on learning problems with no or equality constraints (widely used in the cybersecurity field). Since the solution of such general problems is often continuous but not differentiable with data changes, we apply the generalized implicit function theorem to compute the semi-derivatives that express how the TSEP solution responds to data perturbations. The semi-derivatives enable us to evaluate TSEP models’ vulnerability (at each data point) and solve the proposed attack model. We demonstrate the generality and effectiveness of the proposed method on two TSEP models using mobile sensing data.},

keywords = {Attack model, Data poisoning attacks, Lipschitz continuity, Semi-derivatives, TR-Part_C-SI, Traffic state estimation and prediction},

pubstate = {published},

tppubtype = {article}

}

@article{FIELBAUM2024104580,

title = {Design of mixed fixed-flexible bus public transport networks by tracking the paths of on-demand vehicles},

author = {Andres Fielbaum and Javier Alonso-Mora},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24001013},

doi = {https://doi.org/10.1016/j.trc.2024.104580},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104580},

abstract = {On-demand ridepooling (ODRP) vehicles follow routes that are fully flexible. However, when the system does not provide door-to-door service and users can be asked to walk, their paths tend to concentrate, particularly along main streets that connect highly demanded areas of the city. These frequently travelled segments are hence useful to multiple passengers, which can be used as an indicator that it would be efficient to allocate a fixed public transport line there. In this paper, we formalise this idea and propose a novel method to design a public transport network, where bus fixed lines are combined with ODRP. Given a network and a transport demand, we first simulate how to serve it using only ODRP with walking. For this, we employ a state-of-the-art assignment algorithm, and take as output the resulting users’ paths. These paths are then processed by a tailored algorithm to create fixed lines where the paths accumulate the most. Users who do not have an available fixed line (i.e., those whose paths were barely shared) are served by ODRP in the mixed system. Simulations using real-life data from Utrecht, The Netherlands, and the Sunshine Coast, Australia, reveal the merits of our method compared to several benchmarks. Crucially, our method builds a small number of fixed lines while still serving the majority of the demand through them. This study contributes not only to the design of public transport networks, but also to the understanding of the patterns that naturally appear in intrinsically flexible mobility systems.},

keywords = {Fixed routes, On-demand mobility, Public transport design, Ridepooling, TR-Part_C-SI, Walking legs},

pubstate = {published},

tppubtype = {article}

}

@article{LI2024104576,

title = {Disturbances and safety analysis of linear adaptive cruise control for cut-in scenarios: A theoretical framework},

author = {Zihao Li and Yang Zhou and Danjue Chen and Yunlong Zhang},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24000974},

doi = {https://doi.org/10.1016/j.trc.2024.104576},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104576},

abstract = {Although adaptive cruise control (ACC) has been widely analyzed in the car-following process, the way it reacts to cut-in maneuvers has been largely ignored. The cut-in maneuvers may trigger multiple disturbances to traffic, resulting in traffic oscillations and corresponding rear-end crashes. Hence, this study provides a theoretical analysis framework to model the disturbance evolution for ACC under cut-in scenarios. Specifically, we first derive the general ACC dynamic evolution based on the widely adopted ACC (i.e., linear feedback control) by applying Caley-Hamilton theorem. Given that, two representative cut-in scenarios are designed to comprehensively understand the impact of cut-in vehicle behavior as well as control parameters on the safety and stability of the ACC system. Enable by the interpretability nature of ACC dynamic analytical solution, necessary and sufficient conditions for overshoot and potential safety risks are derived. The proposed framework is further applied to analyze the cut-in disturbance evolution of commercial ACC systems using field-test data and calibrated control parameters, by which the probabilistic safety and stability condition is provided. Through the above efforts, the framework is instrumental in robust ACC design under cut-in scenarios.},

keywords = {Adaptive cruise control (ACC), Automated vehicle (AV), Cut-in scenario, Disturbance analysis, Theoretical Analysis, TR-Part_C-SI},

pubstate = {published},

tppubtype = {article}

}

@article{WANG2024104579,

title = {Electric-vehicle charging facility deployment models for dense-city residential carparks considering demand uncertainty and grid dynamics},

author = {Hua Wang and Qiang Meng and Ling Xiao},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24001001},

doi = {https://doi.org/10.1016/j.trc.2024.104579},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104579},

abstract = {As the driving range of electric vehicles (EVs) increases, home-based charging has been becoming the dominant strategy for EV users to replenish electricity in urban e-mobility systems. In Asian metropolises with high population density (called dense-cities), the majority of residents live in multi-unit dwellings and share public parking and charging facilities in their residential carparks. This study, therefore, investigates the EV charging facility deployment (CFD) problem for dense-city residential carparks, taking into account charging demand uncertainty and the impact of grid dynamics. To address this problem, we first develop a water-pipe model to minimize the construction cost of the CFD scheme while balancing dynamic charging demand and supply. We then extend our approach to formulate a chance-constraint optimization model that considers more practical factors such as stochastic and dynamic charging demand, multi-type chargers, grid dynamics and the setup time of charging service. Furthermore, we propose a simulation-based method to validate the developed CFD models at the operational level. Our case study in Singapore demonstrates that the chance-constraint optimization model produces effective CFD solutions for all simulated charging scenarios. Our results also reveal the importance of considering grid dynamics and charging demand uncertainty for the residential-carpark CFD problem.},

keywords = {Chance-constraint optimization model, Charging demand uncertainty, Charging facility deployment, Electric vehicle, Grid dynamics, TR-Part_C-SI, Water-pipe model},

pubstate = {published},

tppubtype = {article}

}

@article{DOIG2024104581,

title = {How and when cordon metering can reduce travel times},

author = {Jean Doig and Carlos F. Daganzo and Michael J. Cassidy},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24001025},

doi = {https://doi.org/10.1016/j.trc.2024.104581},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104581},

abstract = {The paper addresses two questions regarding cordon metering that have until now gone unanswered. The first of these pertains to how and where a metered cordon ought to be placed in a city to be of greatest benefit. A simple 3-step rule is proposed that can be readily applied in real settings, and that we call the cordon layout conjecture, or CLC. Its use is shown to minimize the overall travel time inside and outside the cordon combined. The second question pertains to the conditions for which an optimally placed metered cordon can reduce said travel time relative to doing nothing. The answer to this question may be disappointing to some readers. We find that metering can reduce this travel time only for congestion levels that well exceed what we observe in real cities of the world. Of course, cordon metering might still have its place, e.g., to transfer congestion from downtown areas with extensive bus operations to outlying areas without buses; or perhaps to curb demand for car travel. The CLC could help in these endeavors. Findings came by simulating traffic in regions with three geographic configurations. They are: an isolated city center within which all trips begin and end; the same city center with suburbs that bring traffic into the city via arterials; and the city center with suburbs and exurbs that dump traffic into the city via freeway off-ramps. These configurations were studied under multiple demand scenarios, including those that generated severe congestion. Parametric tests for a wide range of cordon placements support the CLC. Optimal placements often turned out to reside well inside the city center’s periphery, which is distinct from what has been tried in the literature. Findings also show how suboptimal cordon placements can do damage. And they show how CLC-placed cordons can reduce overall travel time (by close to 15% in some cases), though again, only when congestion levels far exceed anything that we could measure in real cities.},

keywords = {Cordon metering, TR-Part_C-SI, Traffic management, Urban congestion},

pubstate = {published},

tppubtype = {article}

}

@article{ZHOU2024104578,

title = {Implications of stop-and-go traffic on training learning-based car-following control},

author = {Anye Zhou and Srinivas Peeta and Hao Zhou and Jorge Laval and Zejiang Wang and Adian Cook},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24000998},

doi = {https://doi.org/10.1016/j.trc.2024.104578},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104578},

abstract = {Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. This study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.},

keywords = {Behavior cloning, Car-following control, Deep reinforcement learning, Generalizability, System identification, TR-Part_C-SI},

pubstate = {published},

tppubtype = {article}

}

@article{YE2024104723,

title = {Modeling an on-demand meal delivery system with human couriers and autonomous vehicles in a spatial market},

author = {Anke Ye and Kenan Zhang and Michael G. H. Bell and Xiqun (Michael) Chen and Simon Hu},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24002444},

doi = {https://doi.org/10.1016/j.trc.2024.104723},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104723},

abstract = {This paper investigates the impacts of introducing autonomous vehicles (AVs) into an on-demand meal delivery system at a strategic level. The proposed model consists of (i) a microscopic physical model describing the delivery process for bundled orders and (ii) a macroscopic network equilibrium model characterizing the interactions among customers, human couriers (HCs), and AVs, as well as couriers’ repositioning behaviors in the market. A tailored algorithm based on the Alternating Direction Method of Multiplier (ADMM) is developed to solve the platform’s optimal pricing and maximize its profit. To investigate the impact of AV operations, we test three AV distribution rules, i.e., distributing AVs evenly in the space (Rule 0), proportional to demand (Rule 1), and inversely proportional to demand (Rule 2). The numerical experiments show that Rule 2 archives the maximum platform profit, along with the highest service throughput and the hourly earning rate of HCs. Nevertheless, the numerical experiments adopting the parameters calibrated by current market conditions show that the employment of AVs does not show significant benefits to the platform or other stakeholders. It can only generate a higher platform profit when the AV operation cost is lower than HCs’ hourly earnings in a purely labor-based MDS system. As the AV fleet size expands, the improvement of service quality is rather minor meanwhile the hourly earning of HCs drops substantially.},

keywords = {Autonomous vehicles, Bundling delivery, Network equilibrium, On-demand meal delivery, Strategic management, TR-Part_C-SI},

pubstate = {published},

tppubtype = {article}

}

@article{LIU2024104725,

title = {N-MP: A network-state-based Max Pressure algorithm incorporating regional perimeter control},

author = {Hao Liu and Vikash V. Gayah},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24002468},

doi = {https://doi.org/10.1016/j.trc.2024.104725},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104725},

abstract = {The Max Pressure (MP) framework has been shown to be an effective real-time decentralized traffic signal control algorithm. However, despite its superior performance and desirable features – such as the maximum stability property – it may still suffer from deterioration in network mobility due to the rise of congestion within specific regions of an urban traffic network. To address this drawback and further improve the performance of MP control in urban networks, this paper proposes a novel MP algorithm that incorporates regional traffic states into the MP framework. The proposed model – called N-MP – simultaneously integrates perimeter metering control at the boundary of regions of a network to be protected with traditional local intersection control. The proposed model is the first to incorporate perimeter metering control fully within a decentralized signal control environment and inherits the maximum stability property. In addition, it does not require extra traffic state measurements compared to the original MP algorithms, beyond a measure of congestion within the protected region of the network. Microscopic traffic simulation results demonstrate that the proposed model can outperform two baseline perimeter control models – Bang–Bang control and feedback gating – under various traffic conditions. More interestingly, this superiority is maintained in both fully and partially connected environments.},

keywords = {Connected vehicles, Decentralized traffic signal control, Macroscopic fundamental diagram, Max pressure, Multi-scale traffic control, Perimeter control, TR-Part_C-SI},

pubstate = {published},

tppubtype = {article}

}

@article{LI2024104715,

title = {Offline planning and online operation of zonal-based flexible transit service under demand uncertainties and dynamic cancellations},

author = {Manzi Li and Enoch Lee and Hong K. Lo},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24002365},

doi = {https://doi.org/10.1016/j.trc.2024.104715},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104715},

abstract = {This paper introduces a comprehensive framework for planning and operating a zonal-based flexible transit (FT) service, a public transit mode designed to accommodate uncertain demand patterns. The framework addresses both offline planning based on stochastic demand distributions and cancellations, as well as online routing considering real-time orders and cancellation behaviour. Offline interzonal route planning is formulated as a two-stage recourse problem, while the intrazonal routing problem is modelled using a Markov decision process (MDP) that incorporates online information. To solve the problem, a service reliability-based decomposition method is employed to divide the problem into three mixed-integer subproblems. The first subproblem focuses on designing interzonal routes up to a specific demand level, taking into account a designated cancellation probability as determined by reliability measures. An insertion heuristic is developed for this subproblem to improve the solution efficiency. The second subproblem allocates passengers from certain categories to vehicles based on the passenger volume designated by reliability measures. Lastly, the third subproblem refines the vehicle intrazonal route according to the passenger assignment from the previous subproblem. The reliability measures are optimised iteratively until no further improvements are observed in consecutive iterations. The proposed FT service’s performance is evaluated using numerical simulations based on real New York City (NYC) taxi demand data, illustrating the effectiveness of the integrated planning and operational approach in accommodating uncertainties in on-demand transit systems.},

keywords = {Dynamic optimisation, Flexible transit, On-demand transit, Order cancellation, Stochastic demand, Three-phase optimisation, TR-Part_C-SI},

pubstate = {published},

tppubtype = {article}

}

@article{WANG2024104743,

title = {Privacy-preserving data fusion for traffic state estimation: A vertical federated learning approach},

author = {Qiqing Wang and Kaidi Yang},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X2400264X},

doi = {https://doi.org/10.1016/j.trc.2024.104743},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104743},

abstract = {This paper proposes a privacy-preserving data fusion method for traffic state estimation (TSE). Unlike existing works that assume all data sources to be accessible by a single trusted party, we explicitly address data privacy concerns that arise in the collaboration and data sharing between multiple data owners, such as municipal authorities (MAs) and mobility providers (MPs). To this end, we propose a novel vertical federated learning (FL) approach, FedTSE, that enables multiple data owners to collaboratively train and apply a TSE model without having to exchange their private data. To enhance the applicability of the proposed FedTSE in common TSE scenarios with limited availability of ground-truth data, we further propose a privacy-preserving physics-informed FL approach, i.e., FedTSE-PI, that integrates traffic models into FL. Real-world data validation shows that the proposed methods can protect privacy while yielding similar accuracy to the oracle method without privacy considerations.},

keywords = {Data fusion, Data privacy, Federated learning, TR-Part_C-SI, Traffic flow theory, Traffic state estimation},

pubstate = {published},

tppubtype = {article}

}

@article{NG2024104575,

title = {Redesigning large-scale multimodal transit networks with shared autonomous mobility services},

author = {Max T. M. Ng and Hani S. Mahmassani and Ömer Verbas and Taner Cokyasar and Roman Engelhardt},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24000962},

doi = {https://doi.org/10.1016/j.trc.2024.104575},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104575},

abstract = {This study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin–destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.},

keywords = {Autonomous vehicles, Multimodal, Network optimization, Shared Autonomous Mobility Services (SAMS), TR-Part_C-SI, Transit network design},

pubstate = {published},

tppubtype = {article}

}

@article{ZHOU2024104745,

title = {Transit fares integrating alternative modes as a delay insurance},

author = {Yihe Zhou and Wenzhe Sun and Jan-Dirk Schmöcker},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X24002663},

doi = {https://doi.org/10.1016/j.trc.2024.104745},

issn = {0968-090X},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

pages = {104745},

abstract = {Public transport (PT) fare policy remains subject to innovations, not least evident in the Mobility as a Service discussion. Mode integration and related fare strategies can be used to increase the attractiveness of PT by compensating for potential delays. This study proposes “premium fares” as a novel pricing tool that can evaluate and improve the travel time reliability on a multimodal transportation network. The premium fare is higher than the standard fare but allows passengers to use an alternative service free of charge if waiting for the delayed public transport service is anticipated to be longer than a certain qualification threshold. Properties that guarantee monotonicity of the premium fare with respect to distance traveled are developed. The operator aims to find the premium fare price and qualification threshold that can maximize its profit, based on the probability distributions of delay and passengers’ value of time (VOT). We model this optimization problem for a railway line given a limited capacity of alternative mode services, e.g., the number of taxis. A two-stage approach using nonlinear and dynamic programming is developed to obtain the optimal decision variables and associated capacity allocation plan. Our results show that the introduction of the premium fare can benefit both operators and travelers with increased profits and improved travel time reliability. The interplay between fares and the qualification threshold is illustrated using various delay and VOT distributions. To attract enough customers the premium fare has to be set below a specific level dependent on the VOT distribution. Meanwhile, the operator adjusts the qualification threshold to control the cost paid to the alternative service provider.},

keywords = {Delay insurance, Integrated multimodal transport, Nonlinear and dynamic programming, Premium fare, Public transport, TR-Part_C-SI, Travel time reliability},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1287/trsc.2024.0525,

title = {A Day-to-Day Dynamical Approach to the Most Likely User Equilibrium Problem},

author = {Jiayang Li and Qianni Wang and Liyang Feng and Jun Xie and Yu (Marco) Nie},

url = {https://doi.org/10.1287/trsc.2024.0525},

doi = {10.1287/trsc.2024.0525},

journal = {Transportation Science},

volume = {0},

number = {0},

pages = {null},

abstract = {The lack of a unique user equilibrium (UE) route flow in traffic assignment has posed a significant challenge to many transportation applications. The maximum-entropy principle, which advocates for the consistent selection of the most likely solution, is often used to address the challenge. Built on a recently proposed day-to-day discrete-time dynamical model called cumulative logit (CumLog), this study provides a new behavioral underpinning for the maximum-entropy user equilibrium (MEUE) route flow. It has been proven that CumLog can reach a UE state without presuming that travelers are perfectly rational. Here, we further establish that CumLog always converges to the MEUE route flow if (i) travelers have no prior information about routes and thus, are forced to give all routes an equal initial choice probability or if (ii) all travelers gather information from the same source such that the general proportionality condition is satisfied. Thus, CumLog may be used as a practical solution algorithm for the MEUE problem. To put this idea into practice, we propose to eliminate the route enumeration requirement of the original CumLog model through an iterative route discovery scheme. We also examine the discrete-time versions of four popular continuous-time dynamical models and compare them with CumLog. The analysis shows that the replicator dynamic is the only one that has the potential to reach the MEUE solution with some regularity. The analytical results are confirmed through numerical experiments.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This research was funded by the United States National Science Foundation’s Division of Civil, Mechanical and Manufacturing Innovation [Grant 2225087]. The work of J. Xie was funded by the National Natural Science Foundation of China [Grant 72371205].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0525.},

keywords = {TS-SI},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1287/trsc.2024.0520,

title = {A Paradox of Telecommuting and Staggered Work Hours in the Bottleneck Model},

author = {Takara Sakai and Takashi Akamatsu and Koki Satsukawa},

url = {https://doi.org/10.1287/trsc.2024.0520},

doi = {10.1287/trsc.2024.0520},

journal = {Transportation Science},

volume = {0},

number = {0},

pages = {null},

abstract = {We study the long- and short-term effects of telecommuting (TLC), staggered work hours (SWH), and their combined scheme on peak-period congestion and location patterns. In order to enable a unified comparison of the schemes’ long- and short-term effects, we develop a novel equilibrium analysis approach that consistently synthesizes the long-term equilibrium (location and percentage of telecommuting choice) and short-term equilibrium (preferred arrival time and departure time choice). By exploiting their special mathematical structures similar to optimal transport problems, we derive the closed-form solution to the long- and short-term equilibrium while explicitly considering their interaction. These closed-form solutions elucidate the discrepancies between the effects of each scheme and uncover a paradoxical finding: the introduction of SWH, in conjunction with TLC, may increase the total commuting costs compared with the scenario with only TLC, without yielding any improvement in worker utility.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), the 3rd period of SIP “Smart Infrastructure Management System” [Grant JPJ012187] (Funding agency: PublicWorks Research Institute, Japan). This work was also supported by Japan Society for the Promotion of Science (JSPS) KAKENHI [Grants JP20J21744, JP21H01448, JP24K00999, JP20K14843, and JP23K13418] and the Support Program for Urban Studies of the Obayashi Foundation.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0520.},

keywords = {TS-SI},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1287/trsc.2023.0498,

title = {Destroying Phantom Jams with Connectivity and Automation: Nonlinear Dynamics and Control of Mixed Traffic},

author = {Tamas G. Molnar and Gábor Orosz},

url = {https://doi.org/10.1287/trsc.2023.0498},

doi = {10.1287/trsc.2023.0498},

journal = {Transportation Science},

volume = {0},

number = {0},

pages = {null},

abstract = {Connected automated vehicles (CAVs) have the potential to improve the efficiency of vehicular traffic. In this paper, we discuss how CAVs can positively impact the dynamic behavior of mixed traffic systems on highways through the lens of nonlinear dynamics theory. First, we show that human-driven traffic exhibits a bistability phenomenon, in which the same drivers can both drive smoothly or cause congestion, depending on perturbations like a braking of an individual driver. As such, bistability can lead to unexpected phantom traffic jams, which are undesired. By analyzing the corresponding nonlinear dynamical model, we explain the mechanism of bistability and identify which human driver parameters may cause it. Second, we study mixed traffic that includes both human drivers and CAVs, and we analyze how CAVs affect the nonlinear dynamic behavior. We show that a large-enough penetration of CAVs in the traffic flow can eliminate bistability, and we identify the controller parameters of CAVs that are able to do so. Ultimately, this helps to achieve stable and smooth mobility on highways.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This work was supported by the University of Michigan’s Center for Connected and Automated Transportation [U.S. DOT Grant 69A3551747105].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0498.},

keywords = {TS-SI},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1287/trsc.2024.0507,

title = {Dynamic Usage Allocation and Pricing for Curb Space Operation},

author = {Jisoon Lim and Neda Masoud},

url = {https://doi.org/10.1287/trsc.2024.0507},

doi = {10.1287/trsc.2024.0507},

journal = {Transportation Science},

volume = {0},

number = {0},

pages = {null},

abstract = {The importance of curbside management is quickly growing in a modernized urban setting. Dynamic allocation of curb space to different usages and dynamic pricing for those usages can help meet the growing demand for curb space more effectively and promote user turnover. To model curbside operations, we formulate a Stackelberg leader-follower game between a leader operating curbside spaces, who sets space allocation and pricing of each curbside usage, and multi-followers, one for each type of curbside usage, who accept the proposed prices or reject them in favor of outside options. The proposed model offers flexible adaptability to manage curb space usages characterized by high turnover rates, such as parking and ride-sourcing pickup and drop-off, alongside accommodating usages that require more permanent infrastructure allocation, such as micromobility stations. Furthermore, the proposed model is able to capture the sensitivity of users to both prices, which are determined solely by the operator, and the occupancy levels of the curb space, which are determined by the complex interactions between the curbside operator and the users. We model a Stackelberg leader-follower game as a bilevel nonlinear optimization problem and reconstruct the problem into a single-level convex program by applying the Karush-Kuhn-Tucker conditions, objective function transformation, and constraint linearization. Then, we develop a solution algorithm that leverages valid inequalities produced via Benders decomposition. We validate the practicability of the model and draw insights into curbside management using numerical experiments.History: This paper has been accepted for the Transportation Sci. Special Issue on the ISTTT25 Conference.Funding: This work was supported by the National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation [Grant 2046372].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0507.},

keywords = {TS-SI},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1287/trsc.2024.0569,

title = {Economic Analysis of On-Street Parking with Urban Delivery},

author = {Zhengtian Xu and Xiaotong Sun},

url = {https://doi.org/10.1287/trsc.2024.0569},

doi = {10.1287/trsc.2024.0569},

journal = {Transportation Science},

volume = {0},

number = {0},

pages = {null},

abstract = {The surge in online shopping has dramatically increased the demand for short-term curb access for package pickups and deliveries, leading to heightened competition for limited curb space. This paper addresses the problem of how the unique parking demand of deliverers, particularly their parking duration for delivery attempts linked to parking space availability, affects the dynamics of urban curb parking systems. We develop continuum models of a curb parking system and perform analytical analyses to understand the dynamics and steady-state properties of the system under the influence of increased urban deliveries. We conduct comparative statics to examine how various curb management measures, such as pricing, parking duration caps, and dedicated delivery bays, influence the equilibrium conditions, followed by comparisons of the theoretical capacity of these measures. We further demonstrate the working mechanism of delivery bays and their role in forestalling specific failures within a hybrid system with both general parkers and deliverers. Finally, we investigate curb management strategies in nonstationary operational contexts and prescribe the optimal strategies therein. Our findings offer valuable insights into the unique properties that deliverers introduce to curb parking dynamics, highlighting the need for a strategic reevaluation of current management practices. We find that pricing strategies for metered parking to general parkers prove to be more efficient and flexible compared with other interventions. Notably, our analysis suggests that when curb parking pricing is optimally calibrated, the necessity for dedicated delivery bays diminishes. Furthermore, we reveal that optimal curb management strategies could diverge in response to surges in demand, depending on whether the increase sources from general parkers or deliverers. To be effective, the sizing of delivery bays must align with the underlying causes of parking scarcity.History: This paper has been accepted for the Transportation Science Special Section on ISTTT25 Conference.Funding: This work was supported by the George Washington University [University Facilitating Fund].Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0569.},

keywords = {TS-SI},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1287/trsc.2024.0522,

title = {Modeling Multimodal Curbside Usage in Dynamic Networks},

author = {Jiachao Liu and Sean Qian},

url = {https://doi.org/10.1287/trsc.2024.0522},

doi = {10.1287/trsc.2024.0522},

journal = {Transportation Science},

volume = {0},

number = {0},

pages = {null},

abstract = {The proliferation of emerging mobility technology has led to a significant increase in demand for ride-hailing services, on-demand deliveries, and micromobility services, transforming curb spaces into valuable public infrastructure for which multimodal transportation competes. However, the increasing utilization of curbs by different traffic modes has substantial societal impacts, further altering travelers’ choices and polluting the urban environment. Integrating the spatiotemporal characteristics of various behaviors related to curb utilization into general dynamic networks and exploring mobility patterns with multisource data remain a challenge. To address this issue, this study proposes a comprehensive framework of modeling curbside usage by multimodal transportation in a general dynamic network. The framework encapsulates route choices, curb space competition, and interactive effects among different curb users, and it embeds the dynamics of curb usage into a mesoscopic dynamic network model. Furthermore, a curb-aware dynamic origin-destination demand estimation framework is proposed to reveal the network-level spatiotemporal mobility patterns associated with curb usage through a physics-informed data-driven approach. The framework integrates emerging real-world curb use data in conjunction with other mobility data represented on computational graphs, which can be solved efficiently using the forward-backward algorithm on large-scale networks. The framework is examined on a small network as well as a large-scale real-world network. The estimation results on both networks are satisfactory and compelling, demonstrating the capability of the framework to estimate the spatiotemporal curb usage by multimodal transportation.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25.Funding: This material is based upon work supported by the Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy [Award DE-EE0009659].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0522.},

keywords = {TS-SI},

pubstate = {published},

tppubtype = {article}

}