Philly's'transit vigilante' created a real-time bus tracker for his neighbors With a sports timer and some clever coding, Max Goldberg built a DIY display that tells South Philly commuters exactly when their next bus will arrive. Breakthroughs, discoveries, and DIY tips sent every weekday. Philadelphia's mass transit system has had a rough go of it lately. The Pennsylvania city's main public transit provider, SEPTA, has been dealing with massive service cuts, including the elimination of entire bus routes. But South Philly resident Max Goldberg is undeterred.

Large language models (LLMs) are increasingly used as behavioral proxies for self-interested travelers in agent-based traffic models. Although more flexible and generalizable than conventional models, the practical use of these approaches remains limited by scalability due to the cost of calling one LLM for every traveler. Moreover, it has been found that LLM agents often make opaque choices and produce unstable day-to-day dynamics. To address these challenges, we propose to model each homogeneous traveler group facing the same decision context with a single representative LLM agent who behaves like the population's average, maintaining and updating a mixed strategy over routes that coincides with the group's aggregate flow proportions. Each day, the LLM reviews the travel experience and flags routes with positive reinforcement that they hope to use more often, and an interpretable update rule then converts this judgment into strategy adjustments using a tunable (progressively decaying) step size. The representative-agent design improves scalability, while the separation of reasoning from updating clarifies the decision logic while stabilizing learning. In classic traffic assignment settings, we find that the proposed approach converges rapidly to the user equilibrium. In richer settings with income heterogeneity, multi-criteria costs, and multi-modal choices, the generated dynamics remain stable and interpretable, reproducing plausible behavioral patterns well-documented in psychology and economics, for example, the decoy effect in toll versus non-toll road selection, and higher willingness-to-pay for convenience among higher-income travelers when choosing between driving, transit, and park-and-ride options.

Understanding urban human mobility patterns at various spatial levels is essential for social science. This study presents a machine learning framework to downscale origin-destination (OD) taxi trips flows in New York City from a larger spatial unit to a smaller spatial unit. First, correlations between OD trips and demographic, socioeconomic, and commuting characteristics are developed using four models: Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). Second, a perturbation-based sensitivity analysis is applied to interpret variable importance for nonlinear models. The results show that the linear regression model failed to capture the complex variable interactions. While NN performs best with the training and testing datasets, SVM shows the best generalization ability in downscaling performance. The methodology presented in this study provides both analytical advancement and practical applications to improve transportation services and urban development.

There is a little shuttle bus in the Inland Empire that's fueled with big aspirations. It's electric, tops out at 25 mph, and can only go on a pre-designated route set up by the Riverside Transit Agency. But here's a catch -- it also drives itself. As of Monday, commuters in Riverside are the first in the country to ride a fully self-driving, publicly accessible bus that is deployed by a city transit agency. "I like to say I have no lesser ambition than to be the new Detroit for vehicle manufacturing," Riverside Mayor Lock Dawson said.

Los Angeles will utilize AI-powered scanners at Union Station over the next month in an effort to stop passengers with hidden weapons from boarding the rails. Commuters descending to underground platforms for the A, B and D lines (formally known as the Blue, Red and Purple lines) will enter into the testing ground for Metro's 30-day pilot program, which went into effect on Tuesday, though the scanners will not run every day. The program arrives amid growing concern over passenger safety, with Metro recording an uptick in arrests this year for riders carrying concealed weapons. The roughly 6-foot-tall Evolv Technology scanners use artificial intelligence to pinpoint on a person's body where they could possibly be carrying a weapon, according to the company's website. All weapons are banned on the Metro system, and it is illegal to carry a concealed firearm without a permit in California.

The decision making involved behind the mode choice is critical for transportation planning. While statistical learning techniques like discrete choice models have been used traditionally, machine learning (ML) models have gained traction recently among the transportation planners due to their higher predictive performance. However, the black box nature of ML models pose significant interpretability challenges, limiting their practical application in decision and policy making. This study utilised a dataset of $1350$ households belonging to low and low-middle income bracket in the city of Bengaluru to investigate mode choice decision making behaviour using Multinomial logit model and ML classifiers like decision trees, random forests, extreme gradient boosting and support vector machines. In terms of accuracy, random forest model performed the best ($0.788$ on training data and $0.605$ on testing data) compared to all the other models. This research has adopted modern interpretability techniques like feature importance and individual conditional expectation plots to explain the decision making behaviour using ML models. A higher travel costs significantly reduce the predicted probability of bus usage compared to other modes (a $0.66\%$ and $0.34\%$ reduction using Random Forests and XGBoost model for $10\%$ increase in travel cost). However, reducing travel time by $10\%$ increases the preference for the metro ($0.16\%$ in Random Forests and 0.42% in XGBoost). This research augments the ongoing research on mode choice analysis using machine learning techniques, which would help in improving the understanding of the performance of these models with real-world data in terms of both accuracy and interpretability.

We study the problem of optimally routing plug-in electric and conventional fuel vehicles on a city level. In our model, commuters selfishly aim to minimize a local cost that combines travel time, from a fixed origin to a desired destination, and the monetary cost of using city facilities, parking or service stations. The traffic authority can influence the commuters' preferred routing choice by means of personalized discounts on parking tickets and on the energy price at service stations. We formalize the problem of designing these monetary incentives optimally as a large-scale bilevel game, where constraints arise at both levels due to the finite capacities of city facilities and incentives budget. Then, we develop an efficient decentralized solution scheme with convergence guarantees based on BIG Hype, a recently-proposed hypergradient-based algorithm for hierarchical games. Finally, we validate our model via numerical simulations over the Anaheim's network, and show that the proposed approach produces sensible results in terms of traffic decongestion and it is able to solve in minutes problems with more than 48000 variables and 110000 constraints.

Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may be rather challenging. The MRT3's daily ridership fluctuates owing to variables such as holidays, working days, and other unexpected issues. Commuters do not know how many other commuters are on their route on a given day, which may hinder their ability to plan an efficient itinerary. Currently, the DOTr depends on spreadsheets containing historical data, which might be challenging to examine. This study presents a time series prediction of daily traffic to anticipate future attendance at a particular station on specific days.

Did you know there's a specific term for the times when you encounter sudden, inexplicable vehicle congestion on the interstate despite no discernible culprit such as rubbernecking or an accident? It's called a "phantom traffic jam," and was first identified around 12 years ago by researchers in Japan conducting a simple experiment. Despite telling 20 human drivers to all drive at a constant speed around a circular track, even the briefest instances of individuals' pressing their brake pedals compounded on one another, resulting in those recognizable traffic fits and starts. This automotive variation on the "butterfly effect" has been carefully studied ever since, and a research group is now approaching the finish line on a potential solution devoid of any sort of half-baked "self-driving" system. As Associated Press recounts, a recent experiment has shown instances of phantom traffic jams can be reduced by linking cars' into a single communication network via utilizing newer vehicles' adaptive cruise control systems.

Except, possibly, professionals like her who are tasked with reducing it. Ricks has made her career out of caring about traffic patterns. Before her current role as the associate administrator for research, innovation, and demonstration at the FTA, she was the director of mobility and infrastructure for the City of Pittsburgh in Pennsylvania. She has spent countless hours thinking about cars, public transit, roads, and pedestrians--and how to make it all flow more smoothly. "When you're in the peak times for travel, when the system is so full, it only takes a small disruption to cause really big problems," Ricks says.