Measuring the Geographical Diversity of Taxi Trip Origins and Destinations in New York City Using Entropy-Based Metrics

Belangrijkste concepten

Entropy-based measurements of taxi trip origins and destinations provide novel insights into the geographical distribution diversity of human mobility patterns that complement traditional trip count metrics.

Samenvatting

This study develops entropy-based measurements to quantify the geographical distribution diversity of taxi trip origins and destinations in New York City (NYC). The origin-entropy for a taxi zone accounts for all the trips that originate from this zone and calculates the level of geographical distribution diversity of these trips' destinations. Likewise, the destination-entropy for a taxi zone considers all the trips that end in this zone and calculates the level of geographical distribution diversity of these trips' origins.

The key highlights and insights from the study are:

1. Entropy-based measurements effectively capture shifts in the diversity of trips' geographical origins and destinations, reflecting changes in travel decisions due to major events like the COVID-19 pandemic.

2. While traditional trip count metrics show an overall decrease in taxi usage during the pandemic, the entropy measurements reveal that the geographical distribution diversity of trip origins and destinations remained high or even increased in certain zones. This suggests that despite fewer trips, the remaining taxi travelers were still accessing a wide range of destinations from diverse origins.

3. The study identified distinct temporal patterns in origin count, destination count, origin-entropy, and destination-entropy for different taxi zones, providing nuanced insights into how human mobility behaviors varied across the city. For example, zones with major transportation hubs, recreational areas, and mixed-use neighborhoods exhibited unique changes in these metrics.

4. The interactive geovisualization tool developed in this study enables researchers and urban planners to explore the spatial and temporal dynamics of taxi trip patterns, empowering data-driven decision making for transportation management and urban development.

Statistieken

The number of taxi trips starting from JFK Airport decreased significantly during the COVID-19 pandemic, from around 4,000 trips per day in early 2020 to less than 1,000 trips per day in 2020.
The origin-entropy for Central Park increased during the pandemic, indicating that despite fewer trips, the remaining taxi travelers were accessing a wider range of destinations from diverse origins.
The destination-entropy for Battery Park decreased during the pandemic, suggesting that taxi travelers were coming from a more limited set of origins to reach this destination.

Citaten

"Entropy, a concept rooted in physics and information theory, measures the randomness and unpredictability of a system."
"A higher entropy value indicates a more diverse and random mobility pattern, while a lower value indicates a more ordered and predictable sequence of visited locations."
"These entropy-based measurements developed in this study can serve as a valuable adjunct to the conventional measurement of human mobility, which quantifies the number of trips people make between their origins and destinations."

Belangrijkste Inzichten Gedestilleerd Uit

An entropy-based measurement for understanding origin-destination trip distributions: a case study of New York City taxis

by Yuqin Jiang,... om arxiv.org 04-18-2024

https://arxiv.org/pdf/2401.17467.pdf

Diepere vragen

How can the entropy-based measurements be extended to analyze human mobility patterns across different transportation modes, such as public transit, private vehicles, and shared micromobility?

Entropy-based measurements can be extended to analyze human mobility patterns across different transportation modes by adapting the concept to capture the geographical distribution diversity of trips originating from and ending in various transportation modes. For public transit, the origin-entropy and destination-entropy can be calculated to understand the diversity of travel patterns in terms of the origins and destinations of public transit users. This can provide insights into how well-connected public transit systems are and how diverse the travel patterns are within the system.
Similarly, for private vehicles, the entropy-based measurements can be used to analyze the geographical distribution diversity of trips taken by private vehicle owners. This can help in understanding the spread of travel destinations and origins among private vehicle users, highlighting areas with high or low diversity in travel patterns.
In the case of shared micromobility, such as bike-sharing or scooter-sharing services, entropy-based metrics can be applied to assess the diversity of trip origins and destinations among users of these services. By calculating origin-entropy and destination-entropy for shared micromobility trips, planners can gain insights into the effectiveness of these services in providing diverse and convenient transportation options for users.
Overall, extending entropy-based measurements to analyze human mobility patterns across different transportation modes can provide a comprehensive understanding of travel behaviors and help in optimizing transportation systems to meet the diverse needs of urban populations.

How can the insights from this study's entropy-based analysis be integrated with agent-based modeling or other simulation approaches to improve transportation planning and urban design?

The insights from this study's entropy-based analysis can be integrated with agent-based modeling or other simulation approaches in transportation planning and urban design to enhance the accuracy and effectiveness of decision-making processes. Here are some ways in which this integration can be beneficial:

Behavioral Modeling: By incorporating the entropy-based metrics into agent-based models, planners can simulate more realistic human mobility patterns based on the diversity of trip origins and destinations. This can lead to more accurate predictions of travel behaviors and help in designing transportation systems that cater to the varied needs of urban residents.

Scenario Analysis: Using the insights from entropy-based analysis, different scenarios can be created within agent-based models to simulate the impact of policy changes, infrastructure developments, or major events on human mobility patterns. This can aid in evaluating the effectiveness of proposed interventions and making informed decisions for transportation planning.

Optimization: By integrating entropy-based metrics with simulation approaches, planners can optimize transportation systems by identifying areas with low geographical distribution diversity and implementing targeted strategies to improve connectivity and accessibility. This can lead to more efficient urban design and transportation planning.

Visualization: Visualization techniques can be used to represent the results of the entropy-based analysis within agent-based models, allowing stakeholders to visually explore and understand the implications of different scenarios on human mobility patterns. This can facilitate collaborative decision-making and enhance communication among urban planners, policymakers, and the public.

In conclusion, integrating the insights from entropy-based analysis with agent-based modeling or other simulation approaches can provide a holistic understanding of human mobility dynamics and contribute to more effective transportation planning and urban design strategies.