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Routing Using Street Centerline Network (Distance)

Using the Philadelphia Street Centerlines dataset, the routes were computed with street segment length (distance) as the cost. This method produced a route that minimized the total distance traveled. However, the resulting path may not reflect real-world travel times, as it does not account for traffic or road characteristics.

• Key Observation: The route was strictly distance-optimized but lacked temporal realism, especially for congested urban environments.

 

Routing Using ESRI’s Dataset (Distance)

The second analysis utilized ESRI’s street data with driving distance as the cost. Compared to the street centerline network, ESRI’s dataset offers greater spatial accuracy and considers minor variations in the road network.

• Key Observation: The route changed slightly, optimizing segments based on updated and detailed street geometry. However, like the previous method, it does not account for temporal factors such as traffic.

 

Routing Using ESRI’s Dataset (Drive Time)

This method incorporated driving time as the cost, providing a more practical travel route. Drive times were based on general conditions, ignoring temporal variations.

• Key Observation: Routes optimized for drive time demonstrated significant deviations from distance-only routes, particularly in areas with lower speed limits, intersections, and urban congestion. This highlighted the importance of considering real-world conditions when modeling travel routes.

 

Routing Using ESRI’s Dataset (Drive Time - Sunday 8 AM)

The final analysis included drive time at a specific temporal scenario: Sunday at 8 AM. This scenario simulated low-traffic conditions, reflecting typical early-morning travel patterns.

• Key Observation: The resulting route was notably shorter in terms of travel time compared to general drive-time results, indicating reduced congestion. This highlights the variability of optimal routes depending on traffic conditions and time-of-day factors.

 

Comparison of Results

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• Street Centerline Dataset

  • Cost Type: Distance

  • Key Advantage: Distance-optimized path

  • Limitations: Ignores time and road conditions

• ESRI Dataset (Distance)

  • Cost Type: Distance

  • Key Advantage: Improved accuracy in geometry

  • Limitations: No temporal consideration

• ESRI Dataset (Drive Time - General)

  • Cost Type: Drive Time (General)

  • Key Advantage: Practical for real-world travel

  • Limitations: Lacks time-specific congestion insights

• ESRI Dataset (Drive Time - Sunday 8 AM)

  • Cost Type: Drive Time (Sunday 8 AM)

  • Key Advantage: Time-specific optimization

  • Limitations: Limited to modeled conditions (Sunday, 8 AM)

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Conclusion

This analysis demonstrates that routing outcomes vary significantly depending on the network data source, cost parameters, and temporal considerations. While distance-based methods provide a baseline, incorporating drive time data yields routes that better reflect real-world travel conditions. Furthermore, time-specific scenarios, such as Sunday mornings, reveal the importance of integrating temporal data into routing models for urban travel analysis.

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Future work could incorporate dynamic traffic data and additional constraints, such as road closures or accessibility needs, to further enhance route optimization.