About

Mick Hunter is an Associate Professor of East Asian Languages and Literatures at Yale University. He holds a B.A. from Swarthmore College, an M.A. from Sheffield University, and a Ph.D. from Princeton University. His teaching and research focus on various aspects of early Chinese culture, with a particular emphasis on early thought and literature. Hunter is interested in comparisons between early Chinese thinkers and their counterparts across the ancient world, exemplified by his course “Sages of the Ancient World.” He is an advocate for the use of digital research tools in the study of early Chinese texts and has conducted workshops on digitized texts and regular expressions at Yale, Princeton, and Penn. Hunter is working on two long-term book projects: one on Liu Xiang, a bibliographer and compiler of the Western Han dynasty, and another on ancient wisdom literature from the Mediterranean world to China. His recent publication, The Poetics of Early Chinese Thought: How the Shijing Shaped the Chinese Philosophical Tradition (Columbia UP, 2021), argues that the classical Chinese philosophical tradition can be seen as a series of footnotes to the Shijing, emphasizing the importance of oral poetry in shaping early Chinese thought. His first book, Confucius Beyond the Analects (Brill, 2017), offers a comprehensive survey of Confucius material from ancient China and suggests that the Analects was first compiled during the early Han dynasty, roughly three centuries later than traditionally believed. Hunter is also a co-editor of Confucius and the Analects Revisited: New Perspectives on Composition, Dating, and Authorship (Brill, 2018).

Research topics

• Computer Science
• History
• Philosophy

Selected publications

• Analysis of bus dwell times from automated passenger count data and the impact of dwell-time variability on the performance of transit signal priority

  Public Transport · 2025-04-16 · 4 citations

  articleOpen accessSenior author

  Abstract The design of transit signal priority (TSP) systems requires knowledge of dwell-time distributions at bus stops within the block. Dwell-time trends are not well established in the literature despite the ubiquity of large sets of automated passenger count (APC) data. Additionally, the impact of dwell-time variability on TSP performance is not well studied, particularly with field-collected dwell-time data. This study first analyzes trends and distributions inherent in dwell-time data deduced from APC data. Dwell times vary from stop to stop and for each stop by time of day (TOD). Stops with the highest proportions of non-zero dwell time also had the highest dwell-time magnitudes and variability. For most stops, dwell-time data was closely fitted by inverse Gaussian, log-normal, power log-normal, Fisk (log-logistic), and Johnson’s SU distributions. The second part of the study used a simulation environment to evaluate the impact of dwell-time magnitude and variability on TSP performance at both far-side and near-side bus stops. For far-side bus stops, dwell time significantly impacted the bus arrival profile at the check-in detector and thus the selected TSP strategy. Higher dwell-time magnitudes and variability led to a higher share of an Early Green Phase (EG) which is not as effective as a Green Phase Extension (GE). At near-side bus stops, dwell-time variability induced more uncertainty in an estimated time of arrival (ETA) and significantly reduced TSP effectiveness especially for GE. TSP performance in terms of GE success, bus travel time, and side-street traffic delay was significantly better at far-side stops compared to near-side stops.

  PublisherOA PDFDOI

• Early Chinese Manuscript Collections: Sayings, Memory, Verse, and Knowledge, by Rens Krijgsman

  T oung Pao · 2025-03-26

  article1st authorCorresponding
  PublisherDOI

• Evaluation of Transit Signal Priority Strategies and the Impact of Background Signal Timing

  2025-06-05

  articleSenior authorCorresponding

  A recent Transit Cooperative Research Program (TCRP) study that surveyed the transit Signal priority (TSP) practices of 31 transit agencies in the US and Canada showed that despite the widespread adoption of TSP, there is no universal agreement in the selection of TSP strategies and parameters or the quantification of TSP benefits and impacts. To help address this uncertainty, the current study develops simulation experiments to assess the performance of different TSP strategies, identify critical factors and conditions that affect TSP performance, and propose strategies for improved consideration of transit vehicles in signal timing development. The results indicate that TSP performance is most favorable in lower v/c conditions. Compared to Early Green (EG), Green Extension (GE) provides a greater benefit to individual buses. However, as congestion increases, the effectiveness of GE decreases. On a highly congested corridor, with v/c ratios approaching or exceeding 1.0, it is possible that TSP may become infeasible as the conflicting non-TSP movements may have insufficient slack in available capacity to recover from the TSP-related green truncation. The results show that the use of a cycle length slightly longer than the traffic-demand-based optimal cycle could lead to lower impacts to conflicting non-transit vehicles during TSP events.

  PublisherDOI

• Adaptive Traffic Signal Control based on Multi-Agent Reinforcement Learning. Case Study on a simulated real-world corridor

  ArXiv.org · 2025-03-04

  preprintOpen accessSenior author

  Previous studies that have formulated multi-agent reinforcement learning (RL) algorithms for adaptive traffic signal control have primarily used value-based RL methods. However, recent literature has shown that policy-based methods may perform better in partially observable environments. Additionally, RL methods remain largely untested for real-world normally signal timing plans because of the simplifying assumptions common in the literature. The current study attempts to address these gaps and formulates a multi-agent proximal policy optimization (MA-PPO) algorithm to implement adaptive and coordinated traffic control along an arterial corridor. The formulated MA-PPO has a centralized-critic architecture under a centralized training and decentralized execution framework. Agents are designed to allow selection and implementation of up to eight signal phases, as commonly implemented in field controllers. The formulated algorithm is tested on a simulated real-world seven intersection corridor. The speed of convergence for each agent was found to depend on the size of the action space, which depends on the number and sequence of signal phases. The performance of the formulated MA-PPO adaptive control algorithm is compared with the field implemented actuated-coordinated signal control (ASC), modeled using PTV-Vissim-MaxTime software in the loop simulation (SILs). The trained MA-PPO performed significantly better than the ASC for all movements. Compared to ASC the MA-PPO showed 2% and 24% improvements in travel time in the primary and secondary coordination directions, respectively. For cross streets movements MA-PPO also showed significant crossing time reductions. Volume sensitivity experiments revealed that the formulated MA-PPO demonstrated good stability, robustness, and adaptability to changes in traffic demand.

  PublisherOA PDFDOI

• Of the Study of the Book of Naturea

  2024-09-17

  book-chapter1st authorCorresponding
  PublisherDOI

• Original Version of The Martyrdom of Theodoraa

  2024-09-17

  book-chapter1st authorCorresponding
  PublisherDOI

• Experiments and Notes About the Mechanical Origine or Production of Corrosiveness

  2024-09-06

  book-chapter1st authorCorresponding
  PublisherDOI

• Moral Epistlesa

  2024-09-17

  book-chapter1st authorCorresponding
  PublisherDOI

• Of the Cause of Attraction by Suction

  2024-09-06

  book-chapter1st authorCorresponding
  PublisherDOI

• MetaMemento: Enriching Physical Objects with Digital Layers

  2024-10-21 · 1 citations

  articleSenior author

  We present MetaMemento, a mixed reality (MR) tool designed to enhance the personal significance of physical objects by overlaying digital content. This tool allows users to author personal meta-data, such as images, music, and notes, to their physical belongings, creating unique MR mementos. By leveraging the concept of non-fungibility, MetaMemento combines physical and digital ownership. This paper details the development and user experience design of MetaMemento, highlighting its potential to blend digital and physical realities and foster deeper connections with personal items.

  PublisherDOI

Recent grants

• EFRI ARES-CI Embedded Distributed Simulation for Transportation System Management

  NSF · $2.0M · 2007–2013

Frequent coauthors

• Angshuman Guin

  Georgia Institute of Technology

  92 shared

• Michael O. Rodgers

  83 shared

• Edward B. Davis

  82 shared

• Richard Fujimoto

  Georgia Institute of Technology

  81 shared

• Randall Guensler

  Georgia Institute of Technology

  80 shared

• Philip Pecher

  University of Pittsburgh

  43 shared

• Haobing Liu

  Tongji University

  43 shared

• Jorge Laval

  Georgia Institute of Technology

  42 shared