Papers for
urban transportation planners
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Priority dispatch strategies optimize taxi sharing for premium users
Evaluating Priority Dispatch for Premium Membership in On-demand Taxi-sharing
Abstract: In this paper, we analyze the dispatch-level feasibility of introducing premium membership into an on-demand taxi-sharing service using a sequential optimal insertion method. We propose two dispatch-level methods: deadline shortening for premium users, and deadline shortening with dedicated premium vehicles. We compare them with a baseline dispatch method through SUMO-based simulations under three demand scenarios: steady uniform demand, time-varying demand with a peak period, and urban-focused demand with concentrated peak traffic. Our results show that the suitable priority mechanism depends on operating conditions and premium ratio, suggesting that condition-dependent method switching may provide premium benefits while keeping non-premium-user burden smaller for overall optimization.
Causal machine learning improves tolling for fairer and greener traffic
Resolving the Mobility-Equity-Emissions Trilemma: A Causal Machine Learning Framework for Dynamic Tolling Policy
Abstract: We propose a causal machine learning framework to integrate dynamic tolling for mobility, equity, and climate objectives, resolving a long-standing policy trilemma. Using Double Machine Learning, we estimate zone-type-specific treatment effects, showing that commercial zones yield nearly twice the delay reduction of residential zones. Our adaptive quota mechanism expands residential coverage 34-fold while preserving 93% of total delay savings and sustaining high return on investment. The framework delivers climate co-benefits, reducing emissions by 743.5 metric tons of CO2 (valued at 141 USD) via congestion-sensitive pricing under moderate traffic conditions with sensitivity bounds from 557.6 to 929.3 tons. Crucially, we uncover a policy-invariant toll range where performance remains robust across demand scenarios, simplifying implementation. This study reveals critical, previously unidentified insights, such as a policy-invariant toll range, that can significantly simplify real-world implementation. Validated on synthetic data mirroring real-world networks, our methodological advances lay the foundation for future public deployment.
Microscopic simulation improves street design for safety and mobility
Complete Street Design informed by Multi-Agent Simulation
Abstract: Objectives: Complete Street design is currently guided largely by qualitative principles despite the increasing complexity of multimodal urban traffic. This paper develops a microscopic simulation framework for quantitatively evaluating alternative street designs by explicitly representing heterogeneous road users, including Autonomous Vehicles (AVs), Connected Vehicles (CVs), Human-Driven Vehicles (HDVs), pick-up and drop-off (PUDO) services, and micromobility users. The framework jointly evaluates mobility and safety within a unified testbed. Methods:The framework enriches microscopic simulation with behavioral models for heterogeneous road users. It explicitly captures lateral movements, parking maneuvers, pedestrian crossings and jaywalking, and shared-lane bicycle and scooter interactions. Safety is quantified using a conflict severity measure grounded in collision physics, while candidate street layouts are optimized using a genetic algorithm. Findings: Applied to a segment of Belmont Avenue in Chicago, Illinois, the framework shows that dedicated bicycle lanes reduce conflict severity at the expense of vehicular throughput. Increasing AV penetration mitigates congestion by reducing traffic shockwaves while improving interactions with pedestrians relative to CVs and HDVs. The optimization framework efficiently identifies Pareto-efficient street layouts that balance mobility and safety. Novelty: The proposed framework integrates heterogeneous behavioral modeling, physics-based conflict assessment, stochastic performance evaluation, and design optimization within a single quantitative framework for Complete Street design. Practical Applications: The framework provides transportation planners with a predictive virtual testbed for evaluating street designs and quantifying the tradeoff between mobility and safety before implementation.
Graph method improves citywide daily traffic estimation accuracy
Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation
Abstract: The estimation of Annual Average Daily Traffic (AADT) is vital for transportation planning and infrastructure maintenance, yet obtaining accurate values for an entire urban network across multiple years remains challenging due to the high cost and spatial sparsity of physical sensors. This research proposes a novel spatio-temporally complementary feature propagation framework that leverages the strengths of two distinct data sources: spatially sparse but temporally dense loop detector data, and a spatially complete but temporally sparse macroscopic transportation model. The methodology highlights a feature propagation algorithm on directed graphs, formulated as a Poisson energy minimization considering residues. The standard binary adjacency matrix is replaced with flow ratio matrices to capture real-world vehicle turn ratios at intersections. Validated in the city of Zurich, the algorithm demonstrates high computational efficiency, achieving convergence within minutes. Results indicate that the framework effectively reconciles theoretical models with empirical ground truths, yielding a normalized mean absolute error below $10\%$. This scalable approach provides a feasible solution for spatio-temporal network-wide AADT estimation through combining real-world limited sensor coverage and traffic models.