An energy loss-based vehicular injury severity model

Recently published:

  • Ji, Ang and Levinson, D. (2020) An energy loss-based vehicular injury severity model. Accident Analysis and Prevention. 146 October 2020, 105730. [doi]

How crashes translate into physical injuries remains controversial. Previous studies recommended a predictor, Delta-V, to describe the crash consequences in terms of mass and impact speed of vehicles in crashes. This study adopts a new factor, energy loss-based vehicular injury severity (ELVIS), to explain the effects of the energy absorption of two vehicles in a collision. This calibrated variable, which is fitted with regression-based and machine learning models, is compared with the widely-used Delta-V predictor. A multivariate ordered logistic regression with multiple classes is then estimated. The results align with the observation that heavy vehicles are more likely to have inherent protection and rigid structures, especially in the side direction, and so suffer less impact.

 

Shortest paths, travel costs, and traffic

Recently published:

This study focuses on path flow for road network, as the sum of individual route choices from individual travelers, associated with specific path type for each cost factor of auto travel that finds the optimal route with the minimum cumulative cost from the perspective of the corresponding cost analyst interest. The considered cost factors include time, safety, emission, and monetary costs, as well as their composite, internal and full cost of travel. We further explore the extent to which each cost factor explains the observed link traffic flows given an estimated home-to-work demand pattern. The results of the Minneapolis – St. Paul metropolitan area indicate that flows from multiple path types, associated with internal cost components, additionally to the factor of distance, provides the best fit.

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Logistic Curve Models of CO2 Accumulation

Recently published:

  • Levinson, D. (2020) Logistic Curve Models of CO2 Accumulation. Transport Findings. [doi]

This article explores the use of logistic-shaped diffusion curves (S-Curves) to predict the accumulation of atmospheric CO2. The research question here is whether forecasts using logistic curves are stable, that is, do they predict consistently over time with different amounts of data? Using data from the Keeling Curve, we find that the best-fit maximum atmospheric CO2 predicted varies significantly by model year when estimating models limited to data available until that point in time. More recently estimated models are more consistent, all indicate that CO2 accumulation will continue in the absence of an external shock to the system.

Measured and Modeled CO2 over time
Measured and Modeled CO2 over time

A Review of Game Theory Models of Lane Changing

Recently published:

Driver lane-changing behaviours have a significant impact on the safety and the capacity of the vehicle-based traffic system. Therefore, modeling lane-changing ma- neuvers has become an essential component of driving behaviour analysis. Among microscopic LC models, game theory-based lane-changing models highlight the inter- action of drivers, which reveal a more realistic image of driving behaviours compared to other classic models. However, the potential of game theory to describe the hu- man driver’s lane-changing strategies is currently under-estimated. This paper aims to review the recent development of game-theoretic models that are classified ac- cording to their different methodologies and features. They are designed for both human-driven and autonomous vehicles, and we hope they can find applications in future AV industries.

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Estimating the Social Gap with a Game Theory Model of Lane Changing

Recent paper:

Changing lanes is a commonly-used technique for drivers to either overtake slow-moving cars or enter/exit highway ramps. Optional lane changes may save drivers travel time but increase the risk of collision with others. Drivers make such decisions based on experience and emotion rather than analysis, and thus may fail to select the best solution while in a dynamic state of flux. Unlike human drivers, autonomous vehicles can systematically analyze their surroundings and make real-time decisions accordingly. This paper develops a game theory-based lane-changing model by comparing two types of optimization methods. To realize our expectations, we need to first investigate the payoff function of drivers in discretionary lane-changing maneuvers and then quantify it in an equation of costs that trades-off safety and time-saving. After the evaluation for each alternative strategy combination, the results show that there exists a social gap in the discretionary lane-changing game. To deal with that problem, we provide some suggestions for future policy as well as autonomous vehicle controller designs, offering solutions to reduce the impact of disturbances and crashes caused by inappropriate lane changes, and also, inspire further research about more complex cases.

 

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Walking and Talking: The Effect of Smartphone Use and Group Conversation on Pedestrian Speed

Recently published:

Walking speed, walking in group, and phone use
Walking speed, walking in group, and phone use

Distracted walking due to smartphone use is on the rise resulting in growing concern over pedestrian safety and well-being. Our study measured the walking speeds of pedestrian groups differentiated by their smartphone use in two different environments – a wide pedestrian bridge at a university, and a narrow footpath on a busy commercial street. The results show that groups of people, phone users, and often followers of phone users, walk significantly slower than solo walkers uninfluenced by phone. Especially on the narrow street, people in groups and phone users are seen to not only slow themselves down but also slow the people walking behind them.

Temporal Variations in Daily Activity Networks Using Smartcard Data

This study explores temporal variations in activity networks for four million passengers, differentiated as workers and non-workers, using public transport based on a large-scale smart card dataset generated over 105 days in Beijing. We aim to capture their day-to-day transition and cumulative temporal expansion in activity network using transit over days, weeks, and months. Particularly, workers and non-workers are automatically identified based on their different daily routines, whose activity networks are characterized by six features concerning space coverage, distance coverage, and frequency coverage in two ways, namely, on a per-day transition and with an accumulation of days. The transition features of the networks are statistically analyzed and compared by time, while how the expansion features evolve with time are modeled. Results show that, on weekdays, workers are more likely to travel longer (have larger distance coverage), but cover less area (have smaller space coverage) than non- workers. While opposite patterns occur on weekends. Traveling in the ‘North-South’ direction is weakly correlated with traveling in the ‘East-West’ direction. Workers on weekdays, as well as non-workers on weekends, make longer ‘North-South’ trips. Manhattan distance, trip count, and perimeter present a ∩ shape in their probability density functions, while the remaining features decline dramatically, with probability density functions fit by the exponential distribution. The distance coverage expands faster than that of space coverage. Most passengers increase coverage of space and distance when time expands (obviously no one decreases coverage over time, but some don’t change). The research enables findings on temporal load-balancing, long-term cumulative expansion in travel demands of workers and non-workers, re-balancing the distribution of existing workplace and residential location opportunities, and constructing transit-oriented developments with mixed functions over time.

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A Review of Game Theory Models of Lane Changing

Recent working paper:

Driver lane-changing behaviours have a significant impact on the safety and the capacity of the vehicle-based traffic system, therefore, modeling lane-changing maneuvers has become an essential component of driving behaviour analysis. Among microscopic LC models, game theory based lane-changing models highlight the interaction or competition of drivers, which reveal a more realistic image of driving behaviours compared to other classic models. However, the potential of game theory to describe the human driver’s lane-changing strategies is currently under-estimated. This paper aims to review the recent development of game theoretic models that are classified according to their different methodologies and features. They are designed for both human-driven (User-optimal) and autonomous vehicles (User and system-optimal), and we hope they can find applications in future AV industries.

classGT

Trains, trams, and terraces: population growth and network expansion in Sydney: 1861-1931

Recent working paper:

This paper examines the changes that occurred in the tram and train networks and density of population in Sydney between the early 1860s and 1930s when both trains and trams were developing. A set of statistical analysis has been conducted using panel data representing 593 districts of Greater Sydney at suburb (neighborhood) level over each decade from 1861 to 1931. We find that trams and population density are positively associated in a positive feedback process, tram deployment leads population growth and population growth leads tram deployment, both satisfying a Granger causality test.

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