PaperlayerView exact revision on arXiv

Source-linked AI summary

Neighbor-embedded Graph Neural Network-based Crowd Delivery Traffic Management in Smart City

Kishu Gupta, Deepika Saxena, Ashutosh Kumar Singh, Chung-Nan Lee

arXiv:2608.22555v1cs.CRcs.LG

TL;DR

Existing smart-city traffic management approaches often address congestion prediction or time optimization without integrating crowd-delivery optimization and intelligent vehicle selection. NeCDM combines neighbor-embedded GNN traffic prediction with real-time vehicle selection, reporting accurate traffic-flow prediction and reduced sustainability-critical parameters.

  • Problem

    Existing approaches inadequately integrate congestion prediction with crowd-delivery optimization, vehicle reduction, and intelligent vehicle selection for sustainable smart-city transportation.

    Source: A. Observed Limitations · pp. 1–2

  • Method

    NeCDM combines a neighbor-embedded Graph Neural Network for delivery-station traffic prediction with a decision-making unit for selecting suitable vehicles for crowd delivery requests.

    Source: B. Our Contributions · p. 2, A. System Model · p. 3, B. Traffic Congestion Prediction · p. 6

  • Results

    NeCDM accurately predicts traffic flow across delivery stations and supports vehicle selection intended to reduce active delivery vehicles and sustainability-critical parameters.

    Source: C. Results · p. 9, §VI · p. 11

  • Takeaways & Limitations

    Crowd delivery integrated with traffic prediction and vehicle selection is presented as a framework for improving congestion mitigation and delivery efficiency in smart-city environments.

    Source: §I · p. 1, B. Our Contributions · p. 2, §VI · p. 11

    The paper identifies complex, volatile, continuously growing traffic data and interdependencies among vehicles, routes, and delivery operations as constraints on existing traffic-management effectiveness.

    Source: A. Observed Limitations · pp. 1–2

Abstract

from arXiv · show

The significant upsurge in vehicle traffic presents a considerable challenge in the pursuit of smart mobilization and transportation (SMT) worldwide. Current approaches primarily focus on vehicular traffic management through congestion prediction but fall short in addressing essential objectives such as traffic reduction and appropriate vehicle selection to alleviate congestion in smart cities ($SmCt$). To address these concerns, this work introduces a novel \textit{Neighbor-Embedded Graph Neural Network-based Crowd Delivery Traffic Management} (NeCDM) Model, comprising two key components: the Traffic Congestion Prediction Unit (TCPu) and the Traffic Observation and Management Unit (TOMu). The TCPu utilizes Graph Neural Network (GNN) optimization to accurately predict traffic flow levels at various delivery stations within $SmCt$ ecosystems. Additionally, the TOMu facilitates the intelligent selection of the most suitable delivery vehicles for fulfilling crowd delivery requests ($CDR$). This work emphasizes the potential of crowd delivery as a feasible solution for achieving SMT goals while adhering to smart city parameters ($\mathcal{SCP}$s), such as reduced carbon emissions, shorter travel times, and minimized travel distances. The proposed model achieves notable improvements in computational efficiency, including reductions of up to 4.03\% in L1 loss ($£$), 16.66\% in L2 loss ($£_{rmse}$), and 7.64\% in computation time.

I. INTRODUCTION

The paper motivates crowd delivery as a way to reduce active delivery vehicles and improve sustainability-critical parameters, then proposes NeCDM to combine congestion prediction with adaptive vehicle management.

  • Rising on-road vehicle volumes strain smart-city traffic management and motivate Smart Mobility and Transportation frameworks.Source: §I · p. 1
  • Crowd delivery consolidates proximate deliveries into one route, reducing active vehicles and potentially improving carbon emissions, travel distance, duration, and cost.Source: §I · p. 1
  • Existing frameworks often separate congestion prediction from time-based optimization and insufficiently integrate vehicle reduction, crowd delivery, and real-time vehicle selection.Source: A. Observed Limitations · pp. 1–2
  • NeCDM combines a neighbor-embedded GNN-based TCPu for traffic-flow prediction with a TOMu for adaptive vehicle choice, rerouting, and traffic countermeasures.Source: B. Our Contributions · p. 2
  • The integrated units continuously monitor congestion and select appropriate vehicles for crowd delivery requests while curbing the number of on-road vehicles.Source: B. Our Contributions · p. 2

II. RELATED WORK

Prior work advances traffic forecasting through deep, recurrent, convolutional, attention-based, and graph-based methods, but leaves gaps in closed-loop management and integrated resource optimization.

  • Traffic forecasting research spans hybrid spatio-temporal deep learning, graph and multi-graph neural architectures, and resource-aware deployment frameworks.Source: §II · p. 2
  • Convolutional, recurrent, attention, and dynamic graph methods improve representation of periodic patterns, temporal components, and spatial correlations.Source: §II · p. 3
  • Related routing studies address cooperative multi-vehicle routing and multi-depot vehicle routing under congestion and emission constraints.Source: §II · p. 3
  • Three gaps remain: forecasting is often disconnected from closed-loop management, spatial graphs may be static or partly dynamic, and resource optimization is rarely integrated with control.Source: §II · p. 3
  • The NeCDM system model includes users, delivery stations, delivery vehicles, a smart city, and TOMu, with vehicle choice affected by fuel type, mileage, distance, trip time, and load capacity.Source: A. System Model · p. 3, B. Assumptions and Challenges · pp. 3–4
  • Its assumptions constrain vehicle types to Electric, CNG, Petrol, and Diesel and crowd delivery requests to one through ten drop stations.Source: B. Assumptions and Challenges · p. 4

C. Problem Statement and Design Goals

The problem is to solve a multi-objective, constrained smart-transportation task that predicts congestion and selects delivery vehicles for crowd delivery requests across multiple stations.

  • The problem combines numerous delivery stations, crowd delivery requests, appropriate vehicle selection, and sustainability-critical parameters within a constrained smart-city transportation setting.Source: C. Problem Statement and Design Goals · p. 4
  • The congestion-prediction goal is to estimate long-term traffic before finalizing a route and vehicle while incorporating real-time traffic conditions.Source: C. Problem Statement and Design Goals · p. 4
  • NeCDM uses TCPu for congestion forecasting across delivery stations and TOMu for real-time vehicle selection and traffic-management decisions.Source: Figure 2 · p. 5, §IV · pp. 5–6
  • The vehicle-selection goal is to process requests from different users and identify an appropriate delivery vehicle for each crowd delivery request.Source: C. Problem Statement and Design Goals · p. 5
  • A smart city is operationalized as a living space using intelligent transportation, data analytics, and predictive modeling for automated decisions that reduce congestion and support transport reliability.Source: §IV · p. 5
  • A crowd delivery request specifies a source station and one or more destination stations fulfilled by a single delivery vehicle.Source: §IV · p. 5

A. Neighbor Dynamic Graph Network Architecture

The traffic graph models delivery stations as nodes and routes as edges, with node and edge features updated dynamically to represent evolving traffic. Neighbor information is aggregated with self-features through layered message passing for congestion prediction.

B. Traffic Congestion Prediction

The TCPu predicts congestion for crowd delivery requests by constructing traffic graphs, normalizing traffic data, and training a neighbor-embedded GNN. Its estimated flow is then passed to vehicle-management decisions.

  • The TCPu predicts congestion for multiple crowd delivery requests submitted by users across source and delivery stations.Each request includes a source station and one or more delivery stations.Source: B. Traffic Congestion Prediction · p. 6
  • Traffic graphs represent requests across stations, and geodesic distance between source and delivery stations is computed from latitude and longitude.The distance calculation uses the approximate Earth radius R = 6373 km.Source: B. Traffic Congestion Prediction · p. 6
  • Missing values are replaced with −1, after which min–max normalization rescales high-variance traffic samples to [0, 1].Source: B. Traffic Congestion Prediction · p. 6
  • The training dataset is divided into training, validation, and testing segments, while neighboring-node features and each node’s self-features support traffic prediction.Source: B. Traffic Congestion Prediction · p. 6
  • The neighbor-embedded graph representation is used to train the prediction function and generate the estimated traffic congestion flow.Source: B. Traffic Congestion Prediction · pp. 6–7
  • The NeCDM prediction function uses Adam optimization with ReLU activation and minimizes L1 loss, with L2 error used to evaluate loss more clearly.The estimated flow ϱ is passed to TOMu for delivery-vehicle selection.Source: B. Traffic Congestion Prediction · p. 7

C. Delivery Vehicle Selection

The TOMu selects a delivery vehicle by combining predicted traffic flow with vehicle, route, distance, and delivery-demand constraints. A weighted feasibility score aggregates these selection parameters.

D. Operational Design and Complexity

NeCDM operationally combines ride-request and traffic-detail stores, graph construction, congestion prediction, performance evaluation, and vehicle selection. Its complexity includes request, graph, station, and neighbor-embedded GNN processing costs.

Operational Design

Complexity

A. Experimental Setup

The experiments use a specified computing environment and evaluate NeCDM on an extended Beijing trajectory dataset split into training, validation, and testing subsets.

  • The experiments run on a 40-core Intel Xeon Silver 4114 server with 128 GB RAM, Ubuntu 16.04, Python 3.9, and PyTorch.Source: A. Experimental Setup · p. 8
  • The evaluation uses 70,124 extended Beijing trajectory samples collected for smart-city traffic analysis.Source: B. Dataset Details · p. 8
  • The dataset is randomly divided into 80% training, 10% validation, and 10% testing subsets.Source: B. Dataset Details · p. 8
  • Spatial inputs include GPS coordinates and station identifiers for 113 delivery stations.Source: B. Dataset Details · p. 9
  • Temporal inputs contain hourly traffic observations from 02-02-2021 23:00 through 23-03-2023 21:00.Source: B. Dataset Details · p. 9

C. Results

NeCDM is evaluated through optimization, traffic-flow prediction, route outcomes, efficiency analysis, and comparisons with GNN baselines. Results describe rapid loss convergence, accurate prediction across delivery stations, and improved performance with more training data and neighborhood modeling.

Model Optimization

  • NeCDM loss decreases sharply through 20 steps, then gradually converges to a minimal value around 200 steps.Figure 3 reports training and validation L1 loss over iterations.Source: C. Results · p. 9, Figure 3 · p. 9
  • The loss range for traffic-flow prediction over epochs [0, 100] is [280.9755, 29.5213].Table IV presents traffic prediction loss and execution-time optimization for a crowd delivery request.Source: C. Results · p. 9, Table IV · p. 9

Traffic Flow Prediction

  • Traffic-flow plots compare actual and predicted values across delivery stations, and the model accurately predicts flow despite an interval of unavailable data.Source: C. Results · p. 9, Figure 4 · p. 10

Crowd Delivery Route

  • The optimized process produces delivery routes across stations using neighbor-embedded GNN traffic-flow prediction.Source: C. Results · p. 9

Efficiency Analysis

  • Both L1 (£) and L2 (£rmse) losses decrease as the training-set size increases, consistent with incremental learning on larger datasets.The efficiency analysis evaluates varying traffic-data allocations and reports improved prediction accuracy with larger training sets.Source: C. Results · p. 9, Table V · p. 9

Loss Comparison

  • NeCDM outperforms GNN and GNN-NeiEx by achieving greater loss reduction in fewer steps and more accurate congestion predictions across delivery stations.GNN-NeiEx excludes edge information, while NeCDM explicitly models nearest-neighbor interactions.Source: D. Comparison · pp. 9–10, 1) Loss Comparison · p. 10

2) Traffic Flow Comparison:

NeCDM provides more accurate traffic-flow predictions across delivery stations by embedding neighbor information in graph learning, with reported gains in loss and computation time over comparable approaches.

Loading 2608.22555v1…