HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-Time Congestion Avoidance

Xiao Wang; Shun Ren Yang; Hui Nien Hung

cs.AIAug 18, 2026

HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-Time Congestion Avoidance

Authors: Xiao Wang, Shun Ren Yang, Hui Nien Hung

Organizations: Department of Computer Science and the Institute of Communications Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan · Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan

Abstract

Urban traffic congestion reduces productivity and increases travel cost and emissions. Network-wide live travel-time shortest-path rerouting can be highly effective in simulation, but assumes that essentially every on-road vehicle is replanned every decision period. We propose HLSR, a selective hybrid live--forecast vehicle rerouting framework that fuses live edge speeds with short-horizon forecasts under limited intervention scope. Building on dual-threshold congestion detection, calibrated upstream selection, and driver-tailored travel-time prediction, HLSR further introduces approaching-vehicle expansion, travel-time-weighted k-shortest-path generation, and a horizon-dependent hybrid live--forecast segment speed used in multi-cost route allocation.

Figures & tables

Fig. 1 : Activity diagram of the traffic rerouting system.

Parameter	Value
Input / output window ( $T_{\mathrm{in}}$ , $T_{\mathrm{out}}$ )	$12$ , $6$
Features $F$	speed, occupancy
Embed / skip dimension	$64$ / $256$
Encoder depth $D$	$6$
Geo / sem / temporal attention heads	$4$ / $2$ / $2$
Geographic hop mask $\Delta_{\mathrm{far}}$	$7$

TABLE I : Default hyperparameters of LSTAN_GERPE for speed–occupancy inputs, adopted from our prior work [ 14 ] .

Fig. 3 : Definition of the driver behavior parameter

Panel of Figure 5 — Fig. 4 : HLSR runtime pipeline. Roadside sensing data feeds both the three-phase rerouting workflow and forecasting module. Phase 3 fuses live and predicted speeds to compute travel costs for candidate paths.

Fig. 5 : Fourteen TAZs on the Tainan testbed (origins $Z_{4},Z_{10},Z_{12},Z_{13},Z_{14}$ ; destinations $Z_{2},Z_{3},Z_{6},Z_{9}$ ).

Fig. 6 : Upstream-depth sweep on the recommended HLSR stack (

8000

vehicles, SUMO seed

42

). The highlighted marker is

\theta_{ur}{=}9

Fig. 7 : Loop‑detector placement on the Tainan SUMO network for per‑minute edge‑speed and occupancy export.

Fig. 8 : Representative fit of model

F(\Delta)

to empirical

F(z)

(Nanjing Road; Civic and Xinyi show the same pattern).

	Travel time		Reroutes per vehicle
Configuration	mean (s)	$\Delta$ vs HLSR	mean	$\Delta$ vs HLSR
Block I: HLSR‑anchored component ablation
HLSR	380.6	—	0.61	—
HLSR w/o $R_{V}$ (EC1)	400.3	$+19.7$	0.45	$-0.16$
HLSR‑fixed $\theta$ (EC2)	385.7	$+5.1$	0.49	$-0.12$
HLSR‑LIVE (EC3)	438.1	$+57.5$	0.80	$+0.19$

TABLE II : Ablation study on the

8000

-vehicle Tainan scenario under SUMO seed

42

. Block I varies one controller component; Block II varies only the Phase‑3 forecaster. Differences are relative to recommended HLSR (Rank forecasting; positive

=

worse).

		8000		16000		20000
Method	Scope	Dur.	RR	Dur.	RR	Dur.	RR
NRR	$\theta_{ur}{=}1$	525.5	0.36	1211.5	1.36	1276.9	1.42
ReFOCUS+	$\theta_{ur}{=}3$	632.1	0.84	1079.8	1.96	1151.5	2.20
HLSR-Rank (classic selective)	selective	609.7	0.68	1113.7	1.57	1102.4	1.58
Du-GAQ	GAQ+EBkSP	425.5	0.64	1384.4	3.84	1463.1	4.09
HLSR-LIVE	OPT selection	438.1	0.80	1281.3	6.36	1410.0	7.26

TABLE III : Baseline comparison across demand levels (SUMO seed

42

). Entries report mean travel duration (s) and mean reroutes per vehicle (RR). HLSR denotes the recommended hybrid selective configuration. At

8000

vehicles, HLSR also attains time loss

215.5

s and waiting time

150.1

s (lowest among compared methods).

Vehicle Routing

Vehicle routing studies how to assign and sequence trips for fleets while meeting capacity, time, energy, and service constraints.

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Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration

Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast

Department of Computer Science Texas A&M University Corpus Christi, USA

Multivariate Time Series Forecasting Traffic Forecasting Time Series Transformers

Open paper AskPDF arXiv

Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks. However, prediction remains challenging because network demand is influenced by complex mobility patterns, congestion dynamics, and heterogeneous user behavior. This paper introduces the Parameter-Efficient Hybrid Transformer (PEHT), a network traffic prediction framework that integrates urban mobility and congestion information into a Transformer-based architecture. PEHT separates primary network communication features from secondary urban mobility features and incorporates Low-Rank Adaptation (LoRA) into the Transformer encoder to reduce the number of trainable parameters while maintaining high predictive accuracy. A multimodal fusion strategy then injects external mobility and congestion features into the decoder to improve traffic forecasting. Experiments on the Telecom Italia Milan dataset and multiple synthetic congestion scenarios show that PEHT outperforms state-of-the-art baselines in terms of RMSE, MAE, and

R^2

. The implementation is available in the GitHub repository.

Abstract

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