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The Evolution of Eco-routing under Population Growth: Evidence from Six U.S. Cities

Zhiheng Shi, Xiaohan Xu, Wei Ma, Kairui Feng, Bin He

Abstract

Rapid urban population growth drives car travel demand, increasing transport carbon emissions and posing a critical challenge to sustainable development. Although existing studies have demonstrated that eco-routing can reduce individual emissions, research gaps remain. On the one hand, such personal reductions have a negligible impact on overall emissions, and cannot be simply aggregated to capture the complex effects of large-scale eco-routing. On the other hand, under population growth, the long-term effectiveness of eco-routing, as well as the evolution of its efficiency and traveler route choice, remain underexplored. To address these limitations, this study proposes Time-Only and Time-Carbon user equilibrium (UE) models, integrates them with a demand forecasting method for simulating future network traffic, and designs multi-dimensional metrics to characterize urban dynamics. Using real-world road networks, commuting origin-destination (OD) demand, and population projections under various shared socioeconomic pathways (SSPs) for six representative U.S. cities as a case study, we conduct a comprehensive analysis of urban dynamics across different routing strategies and population sizes. The results reveal that while eco-routing mitigates total emissions, emissions in most cities scale superlinearly with population, a scaling order that remains invariant regardless of routing and construction strategies. Moreover, under population growth, travelers using eco-routing tend to increasingly select shorter routes, giving rise to carbon bottlenecks. A strategy of targeted capacity expansion on these critical bottlenecks (0.46% of links) significantly reduces both emissions (3%) and travel time (28%) without compromising eco-routing efficiency. This study provides a foundation for formulating low-carbon urban transport planning and emission reduction policies.

The Evolution of Eco-routing under Population Growth: Evidence from Six U.S. Cities

Abstract

Rapid urban population growth drives car travel demand, increasing transport carbon emissions and posing a critical challenge to sustainable development. Although existing studies have demonstrated that eco-routing can reduce individual emissions, research gaps remain. On the one hand, such personal reductions have a negligible impact on overall emissions, and cannot be simply aggregated to capture the complex effects of large-scale eco-routing. On the other hand, under population growth, the long-term effectiveness of eco-routing, as well as the evolution of its efficiency and traveler route choice, remain underexplored. To address these limitations, this study proposes Time-Only and Time-Carbon user equilibrium (UE) models, integrates them with a demand forecasting method for simulating future network traffic, and designs multi-dimensional metrics to characterize urban dynamics. Using real-world road networks, commuting origin-destination (OD) demand, and population projections under various shared socioeconomic pathways (SSPs) for six representative U.S. cities as a case study, we conduct a comprehensive analysis of urban dynamics across different routing strategies and population sizes. The results reveal that while eco-routing mitigates total emissions, emissions in most cities scale superlinearly with population, a scaling order that remains invariant regardless of routing and construction strategies. Moreover, under population growth, travelers using eco-routing tend to increasingly select shorter routes, giving rise to carbon bottlenecks. A strategy of targeted capacity expansion on these critical bottlenecks (0.46% of links) significantly reduces both emissions (3%) and travel time (28%) without compromising eco-routing efficiency. This study provides a foundation for formulating low-carbon urban transport planning and emission reduction policies.
Paper Structure (36 sections, 7 theorems, 40 equations, 17 figures, 10 tables)

This paper contains 36 sections, 7 theorems, 40 equations, 17 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Population Growth and Urban Transport Emissions
  4. Application of Eco-routing in Large-Scale Networks
  5. UE Traffic Assignment
  6. Methodology
  7. ARIMA-based Prediction of OD Demand
  8. Time-Only and Time-Carbon UE models
  9. Time-Only UE Model
  10. Time-Carbon UE Model
  11. Indicator Design
  12. Urban Metrics
  13. Eco-routing Efficiency Metrics
  14. Theoretical Derivations
  15. Scaling Relationship between Emissions and Total Demand
  16. ...and 21 more sections

Key Result

Theorem 3.1

Let $d(1)=\{d^{s, t}(1)\}_{(s,t) \in W}$ denote the base demand. For a demand scaling factor $\lambda > 0$, the scaled demand $d(\lambda)=\{d^{s, t}(\lambda)\}_{(s,t) \in W}$ is defined by $d^{s, t}(\lambda)=\lambda d^{s, t}(1)$; $x(\lambda)=\{x_a(\lambda)\}_{a \in A}$ represents the UE flows corres Within typical road networks, there exist constants $c_1$, $c_2 > 0$, which are independent of $\la

Figures (17)

  • Figure 1: The four optimization levels of eco-routing (with the positive x-axis extending to the left and the positive y-axis extending upwards).
  • Figure 2: The proposed analytical framework of this study.
  • Figure 3: The geospatial distribution of the six representative U.S. cities.
  • Figure 4: Carbon emissions (t) for the six representative U.S. cities across all years, SSPs, and UE models.
  • Figure 5: UETT (min) for the six representative U.S. cities across all years, SSPs, and UE models.
  • ...and 12 more figures

Theorems & Definitions (20)

  • Theorem 3.1: Linear Scaling of Emissions with Total Demand
  • proof
  • Remark 1: Local Scaling Characteristics
  • Corollary 3.2: Invariant Scaling Order under Routing Strategies
  • proof
  • Remark 2: Policy Implications
  • Theorem 3.3: Critical Role of Travel Distance Reduction in Emission Mitigation
  • proof
  • Remark 3: Policy Implications
  • Theorem 3.4: Impact of Capacity Expansion on Emissions
  • ...and 10 more