arXiv subjects

Neema Nassir

Publications and source records attributed to Neema Nassir.

5 recordsLinked to original sources

arXiv2026

Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control

Human-centric traffic signal control in corridor networks must increasingly account for multimodal travelers, particularly high-occupancy public transportation, rather than focusing solely on vehicle-centric performance. This paper proposes STDSH-MARL (Spatio-Temporal Dual-Stage Hypergraph based Multi-Agent Reinforcement Learning), a multi-agent deep reinforcement learning framework that follows a centralized training and decentralized execution paradigm. The proposed method captures spatio-temporal dependencies through a novel dual-stage hypergraph attention mechanism that models interactions across both spatial and temporal hyperedges. In addition, a hybrid discrete action space is introduced to jointly determine the next signal phase configuration and its corresponding green duration, enabling more adaptive signal timing decisions. Experiments conducted on a corridor network under five traffic scenarios demonstrate that STDSH-MARL achieves strong overall multimodal performance, with substantial and relatively consistent reductions in tram waiting time, while improvements in bus waiting time are more variable across traffic scenarios. These results highlight the trade-off among overall network efficiency, tram priority, and bus service quality. Compared with state-of-the-art baseline methods, the proposed approach achieves superior overall performance. Further ablation studies confirm the contribution of each component of STDSH-MARL, with temporal hyperedges identified as the most influential factor driving the observed performance gains.

cs.LG↗

arXiv2026

Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach

Coordinating traffic signals along multimodal corridors is challenging because many multi-agent deep reinforcement learning (DRL) approaches remain vehicle-centric and struggle with high-dimensional discrete action spaces. We propose MA2B-DDQN, a human-centric multi-agent action-branching double Deep Q-Network (DQN) framework that explicitly optimizes traveler-level equity. Our key contribution is an action-branching discrete control formulation that decomposes corridor control into (i) local, per-intersection actions that allocate green time between the next two phases and (ii) a single global action that selects the total duration of those phases. This decomposition enables scalable coordination under discrete control while reducing the effective complexity of joint decision-making. We also design a human-centric reward that penalizes the number of delayed individuals in the corridor, accounting for pedestrians, vehicle occupants, and transit passengers. Extensive evaluations across seven realistic traffic scenarios in Melbourne, Australia, demonstrate that our approach significantly reduces the number of impacted travelers, outperforming existing DRL and baseline methods. Experiments confirm the robustness of our model, showing minimal variance across diverse settings. This framework not only advocates for a fairer traffic signal system but also provides a scalable solution adaptable to varied urban traffic conditions.

cs.LG↗

arXiv2025

Analytical modeling of a stop-less modular bus line: Optimization, feasibility, and economies of scale

Conventional bus services often struggle with inefficiencies including prolonged dwell times at heavily used stops, especially for through passengers. A stop-less autonomous modular bus service (SLAM) has been proposed to reduce dwell times by decoupling the front pod to serve stops and then coupling it to the next bus. However, the optimal service design and feasibility region remain underexplored, despite their importance for planning and deployment. We propose an analytical optimization model that characterizes the optimal design, feasibility conditions, and sources of scale economies. Three novel constraints distinguish SLAM from conventional bus services: (i) a minimum headway to ensure sufficient time for decoupling, alighting, boarding, and coupling operations, (ii) a maximum headway to guarantee all passengers arriving within a headway fit in the standby pod, and (iii) a minimum bus length constraint, requiring at least two pods per bus to run in a SLAM manner. As ridership grows, the optimal design evolves through several regimes, in which headway constraints alternate between slack and binding states, while capacity constraints shift from one active form to another. Our analysis indicates that, compared with conventional services, SLAM is most suitable at intermediate demand levels: at low demand, the fixed costs of standby pods and the minimum two-pod configuration outweigh the time-saving benefits, whereas at high demand, non-stopping operation becomes infeasible. We further decompose the sources of scale economies into four components: the Mohring effect, through-capacity economies, boarding-capacity economies, and standby-pod costs, identifying under which conditions each of them is present. The numerical results validate the theoretical analysis.

eess.SY↗

arXiv2023

A sparse identification approach for automating choice models' specification

The methodology discussed in this paper aims to enhance choice models' comprehensiveness and explanatory power for forecasting choice outcomes. To achieve these, we have developed a data-driven method that leverages machine learning procedures for identifying the most effective representation of variables in mode choice empirical probability specifications. The methodology will show its significance, particularly in the face of big data and an abundance of variables where it can search through many candidate models. Furthermore, this study will have potential applications in transportation planning and policy-making, which will be achieved by introducing a sparse identification method that looks for the sparsest specification ( parsimonious model ) in the domain of candidate functions. Finally, this paper applies the method to synthetic choice data as a proof of concept. We perform two experiments and show that if the functional form used to generate the synthetic data lies in the domain of base functions, the methodology can recover that. Otherwise, the method will raise a red flag by outputting small coefficients ( near zero ) for base functions.

stat.ME↗

arXiv2022

A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction

Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions such as traffic speed across a given road network into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. To model the spatial factors, we propose a variant of the graph convolutional network (GCN) named LPGCN to embed road network graph vertices into a latent space, where vertices with correlated traffic conditions are close to each other. To model the temporal factors, we use a multi-path convolutional neural network (CNN) to learn the joint impact of different combinations of past traffic conditions on the future traffic conditions. Such a joint impact is further modulated by an attention} generated from an embedding of the prediction time, which encodes the periodic patterns of traffic conditions. We evaluate our model on real-world road networks and traffic data. The experimental results show that our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.

cs.LG↗