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Ride-sharing contributes significantly to lowering trip expenses, easing traffic congestion and decreasing air pollution. However, current order pairing approaches in ride-sharing usually focus on minimizing total trip distances or maximizing platform profits, overlooking the drivers' desire for increased earnings. As a result, drivers might provide dishonest information to gain higher profits, leading to inefficient order pairing for the ride-sharing platform and potential losses for both the platform and drivers. In this paper, we address this challenging issue by developing efficient order pairing mechanisms that maximize the social welfare of the platform and drivers. Specifically, we introduce two truthful auction-based order pairing mechanisms, SWMOM-VCG and SWMOM-GM, where drivers bid on platform-published orders to complete them and earn profits. We provide theoretical proof that both mechanisms fulfill the criteria of individual rationality, profitability, truthfulness and so on. Using real taxi order data from New York City, we assess the performance of both mechanisms and show that they achieve greater social welfare compared to existing methods. Additionally, we find that SWMOM-GM requires less computation time than SWMOM-VCG for order pairing, with only a minor reduction in social welfare. [ABSTRACT FROM AUTHOR]

Global trends toward urbanization will exacerbate traffic congestion, delays in economic productivity, and air pollution issues for growing cities. Traffic congestion pricing is one method available to help ameliorate these concerns. New York City is on the verge of implementing a cordon-based traffic congestion pricing policy around its central business district. For budget-constrained municipalities, evaluating implementation of such policy could be costly. This article proposes a sketch-planning methodology, called Cordon Screen, for major U.S. cities to evaluate the net income, traffic mitigation, and avoided pollution emissions from cordon-based traffic congestion pricing. This method relies on national datasets and limited user-specific data inputs, along with a range of user-selectable assumptions informed by academic literature to deliver order-of-magnitude results. The numerous limitations of this method are acceptable for preliminary policy evaluation to determine if greater financial investment to obtain more accurate results is justified. The Denver metropolitan area is used to demonstrate Cordon Screen capabilities, with mid-range assumption results suggesting the policy is most effective at generating net income and increasing vehicle speeds on major interstates. For Denver, the policy is comparably less effective at reducing air pollution and increasing speeds on minor roadways. Validation against early implementation results from the London cordon are acceptable. However, users should discount revenue generation projections. Choice of cordon area may be the most difficult obstacle when using the Cordon Screen. With refinement, Cordon Screen could serve as a low-cost, open-source planning evaluation tool for growing and congested U.S. cities. Implications: As global urbanization trends continue, impacted local governments will be looking to explore policies to mitigate traffic congestion and reduce environmental emissions. Internationally, cordon-based traffic congestion pricing has been implemented in London, Singapore, and several other large cities. In America, New York City is implementing cordon-based congestion pricing around its central business district to reduce traffic and environmental emissions. Financial resource constraints, exacerbated by the COVID-19 pandemic, may limit the ability for local governments to invest in studying new policy options. The Cordon Screen method detailed in the manuscript presents a low-cost, open-source approach to assessing the potential benefits of cordon-based traffic congestion policy. The method utilizes national datasets to minimize user-specific data requirements and allows users to toggle between a range of values to test sensitivities to key assumptions. For example, emissions reductions are highly sensitive to how drivers respond to tolling. In this example, sensitivity testing enables users to understand how policy design can impact air quality goals. The Cordon Screen approach presented provides a strong platform for future stakeholder deliberation, refinement, and implementation. [ABSTRACT FROM AUTHOR]

The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse. The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 and 2019. We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion. In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night. Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures. • We assessed traffic congestion in an environmental justice community over 2 years. • We analyzed time series of crowd-sensed traffic maps. • Traffic increased in the South Bronx in New York City between 2017 and 2019. • The local community will use the data to advocate for mitigating measures. • Crowd-sensed traffic data allow assessing community impacts of traffic globally. [ABSTRACT FROM AUTHOR]

With the soaring popularity of ride-hailing, the interdependence between transit ridership, ride-hailing ridership, and urban congestion motivates the following question: can public transit and ride-hailing coexist and thrive in a way that enhances the urban transportation ecosystem as a whole? To answer this question, we develop a mathematical and computational framework that optimizes transit schedules while explicitly accounting for their impacts on road congestion and passengers' mode choice between transit and ride-hailing. The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. Based on computational case study experiments in New York City, our optimized transit schedules consistently lead to 0.4%–3% system-wide cost reduction. This amounts to rush-hour savings of millions of dollars per day while simultaneously reducing the costs to passengers and transportation service providers. These benefits are driven by a better alignment of available transportation options with passengers' preferences—by redistributing public transit resources to where they provide the strongest societal benefits. These results are robust to underlying assumptions about passenger demand, transit level of service, the dynamics of ride-hailing operations, and transit fare structures. Ultimately, by explicitly accounting for ride-hailing competition, passenger preferences, and traffic congestion, transit agencies can develop schedules that lower costs for passengers, operators, and the system as a whole: a rare win–win–win outcome. [ABSTRACT FROM AUTHOR]

This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies). [ABSTRACT FROM AUTHOR]

Online shopping has shown a remarkable growth trend for the past three decades. The increasing demand for deliveries resulting from online shopping generates lots of negative externalities contributing to traffic congestion, parking violations, and air pollution. To mitigate these negative externalities, the importance of an alternative delivery location (ADL) is growing in transportation area as delivery and transportation systems are intertwined. The ADL includes a delivery locker (e.g., Amazon Hub Locker), a brick-and-mortar store who partners with parcel delivery companies (e.g., UPS Access Points), and a postal store. Despite the growing size and importance of the ADL, little is known about receivers' behavior on the ADL from the transportation perspective. To better understand the ADL, this research developed joint models of parcel demand and the ADL to investigate factors affecting (a) the adoption of the ADL and (b) the distance people are willing to travel using the NYC DOT Citywide Mobility Survey in 2018. The results show that delivery frequency received by respondents negatively affects the adoption but does not affect the tolerable distance. The impacts of socio-demographic attributes on the behavior are also discussed. A prediction to the entire population in Manhattan was made and a corresponding strategy to determine the optimal ADL location is discussed. This research is the first attempt comprehensively investigating behaviors on the ADL from the transportation planning perspective. This research will contribute to the literature in urban freight systems by improving understanding of the ADL in dense urban areas. [ABSTRACT FROM AUTHOR]

Taxi demand forecasting plays an important role in ride-hailing services. Accurate taxi demand forecasting can assist taxi companies in pre-allocating taxis, improving vehicle utilization, reducing waiting time, and alleviating traffic congestion. It is a challenging task due to the highly non-linear and complicated spatial-temporal patterns of the taxi data. Most of the existing taxi demand forecasting methods lack the ability to capture the dynamic spatial-temporal dependencies among regions. They either fail to consider the limitations of Graph Neural Networks or do not efficiently capture the long-term temporal dependencies. In this paper, we propose a Spatial-Temporal Diffusion Convolutional Network (ST-DCN) for taxi demand forecasting. The dynamic spatial dependencies are efficiently captured through a two-phase graph diffusion convolutional network where the attention mechanism is introduced. Moreover, a novel temporal convolution module is designed to learn various ranges of temporal dependencies, including recent, daily, and weekly periods. Inside the module, convolution layers are stacked to handle very long sequences. Experimental results on two large-scale real-world taxi datasets from New York City (NYC) and Chengdu demonstrate that our method significantly outperforms seven state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Ride hailing services, such as Uber, Lyft, and Grab, have become a major transportation mode in the last decade. The number of current passenger requests is one of the important factors for such services routing and pricing algorithms. Therefore, predicting future passenger request for ride hailing services can boost the efficiency of the service for both drivers and riders by pre-planning the allocation of vehicles and avoiding traffic congestions. Demand forecasting for ride hailing services relies upon the spatial and temporal correlations of its features. The existing literatures mostly divide the target area into rectangular grids (based on the longitude and latitude), consider only adjacent grids for spatial correlation, and calculate demand for each grid independently. An individual grid can contain different regions with high and low demand or have a major part of it outside the land area, which obscures the granularity and precision of estimations and predictions. This paper attempts to mitigate the limitations of grid-based methods by estimating and predicting ride hailing service demand between geographic regions as pickup and destination zones. For predicting demand, a convolutional neural network is integrated with a recurrent autoencoder network to best capture the spatial–temporal correlations of features, including time of the day, month, year, weekend, holiday, pickup zone, destination, and demand. In our experiments, we forecast the demand for each pickup–destination pair for the next day at a certain hour by observing the demands over the past 2 weeks during the same hour in the New York City hire vehicle data set. Using the same model (CNN-biLSTM-AE) to predict demand for geographical regions, it achieved an R 2 of 0.984, while predicting demand for cells in the grid achieved an R 2 0.545. While using the geographical regions instead of grids for partitioning the space, we compared our deep learning model with LSTM, CNN, CNN-LSTM, and LSTM-AE models and observed an improvement in R 2 from 0.632 to 0.767 and an improvement in RMSE from 20.53 to 16.33 against CNN. [ABSTRACT FROM AUTHOR]

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially. [ABSTRACT FROM AUTHOR]

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data. [ABSTRACT FROM AUTHOR]

Freight transportation is critical to metropolitan regions' economic competitiveness and long-term viability. However, it is one of the activities that produce the most negative externalities, including greenhouse gas emissions. The study of the relationship between freight transportation, land use, and demand management is critical for promoting enlightened policies that increase the social benefits of supply chains while avoiding their undesirable effects, such as climate change. Nonetheless, the tools available to public planners for incorporating freight transportation into their analyses are limited. In this context, this paper aims to highlight the impact that freight demand management strategies and freight-efficient land uses can have on increasing urban freight accessibility. In this study, the authors suggest a freight accessibility framework based on two measures of accessibility (an isochrone-based model and a gravity-based model) to analyze the spatiotemporal variation of freight accessibility in the New York City metropolitan statistical area for four sectors—retail trade, accommodation and food services, transportation and warehousing, and e-commerce. The results indicate that accessibility measures provide valuable insights towards policymaking, particularly to policies that target land use (e.g., location of warehouses), and policies that aim to shift the traffic of freight vehicles to periods with less traffic congestion. [ABSTRACT FROM AUTHOR]

Aviation delays impose significant external costs on society. This paper represents one of the first attempts to use a count regression model to study delays at the three New York city area airports – Liberty (Newark), John F. Kennedy and LaGuardia. Explanatory variables are drawn from an extensive literature survey and reflect a range of environmental and operational causal factors. Adverse weather has the largest impact on expected delay, followed by congestion during the metroplex peak. Airport operations peaks matter if they occur at times when the metroplex is busy. Other significant causal factors include surface congestion and arrivals from congested domestic hubs. The impact of the latter appears to be driven by the proportion of domestic traffic. The complex causal factors of delays suggest that regardless of the empirical approach, airport specificities are important in reducing delays. [ABSTRACT FROM AUTHOR]

Urban mobility is facing a paradigm shift towards providing more convenient, environmentally friendly, and on-demand services. Satisfying customer needs in a cost-efficient way has been the goal of many ridesharing systems. Capacitated ridesharing is considered as an effective service for reducing traffic congestion and pollution nowadays. Providing more operational strategies that can optimize on-demand ridesharing needs further investigation. In the current work, we focus on developing a matching algorithm for solving the on-demand ridesharing operation task in a real-time setting. We develop a simulation framework that can be used to propose a real-time shuttle ridesharing search algorithm. We propose a novel, computationally efficient, real-time ridesharing algorithm. We formulate the ridesharing assignment algorithm as a combinatorial optimization problem. The computational complexity of the proposed algorithm is reduced from exponential to linear, and the search space of the optimization problem is reduced by introducing heuristics. Our approach implements dynamic congestion by regularly updating the network's road segments' travel time during the simulation horizon to have more realistic results. We demonstrate how our algorithm, when applied to the New York City taxi dataset, provides a clear advantage over the current taxi fleet in terms of service rate. Furthermore, the developed simulation framework can provide valuable insights regarding cost functions and operational policies. • Development of a modular real-time simulation framework for the capacitated ridesharing problem. • Formulation of the ridesharing problem as a dynamic deterministic ondemand matching problem with tolerance times. • Implementation of dynamic congestion by regularly updating link travel times during the simulation horizon. • Solving the optimization problem in an online manner using both heuristics and commercial solvers. • Modeling of stakeholders' multiple objectives and design of policies that lead to efficient and mutually beneficial solutions. [ABSTRACT FROM AUTHOR]

The author presents a personal narrative of his meeting with New York metropolitan city planner Robert Moses in 1968 and Moses's proposal to solve the problem of the congested bridges in New York City by building a nine-mile bridge.

Exposure to environmental noise from traffic is common in urban areas and has been linked to increased risks of adverse health effects including cardiovascular disease. Because traffic sources also produce air pollutants that increase the risk of cardiovascular morbidity, associations between traffic exposures and health outcomes may involve confounding and/or synergisms between air pollution and noise. While prior studies have characterized intraurban spatial variation in air pollution in New York City (NYC), limited data exists on the levels and spatial variation in noise levels. We measured 1-week equivalent continuous sound pressure levels (L) at 56 sites during the fall of 2012 across NYC locations with varying traffic intensity and building density that are routinely monitored for combustion-related air pollutants. We evaluated correlations among several noise metrics used to characterize noise exposures, including L during different time periods (night, day, weekday, weekend), L (day-night noise), and measures of intermittent noise defined as the ratio of peak levels to median and background levels. We also examined correlations between sound pressure levels and co-located simultaneous measures of nitric oxide (NO), nitrogen dioxide (NO), fine particulate matter (PM), and black carbon (BC) as well as estimates of traffic and building density around the monitoring sites. Noise levels varied widely across the 56 monitoring sites; 1-week L varied by 21.6 dBA (range 59.1-80.7 dBA) with the highest levels observed during the weekday, daytime hours. Indices of average noise were well correlated with each other ( r > 0.83), while indices of intermittent noise were not well correlated with average noise levels ( r < 0.41). One-week L correlated well with NO, NO, and EC levels ( r = 0.61 to 0.68) and less so with PM levels ( r = 0.45). We observed associations between 1-week noise levels and traffic intensity within 100 m of the monitoring sites ( r = 0.58). The high levels of noise observed in NYC often exceed recommended guidelines for outdoor and personal exposures, suggesting unhealthy levels in many locations. Associations between noise, traffic, and combustion air pollutants suggest the possibility for confounding and/or synergism in intraurban epidemiological studies of traffic-related health effects. The different spatial pattern of intermittent noise compared to average noise level may suggest different sources. [ABSTRACT FROM AUTHOR]

Highlights: [•] Uses large scale GPS traces of taxi cabs in New York City. [•] A model for estimating link travel times is developed using large scale data. [•] Developed insights into the congestion issues in New York City. [•] Rigorous numerical results are performed to test the approach. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the findings from a stated choice (SC) analysis conducted in the context of proposed changes to the lane system in use for the Lincoln Tunnel crossing into Manhattan. Currently, the approach road (NJ 495) to the Lincoln Tunnel has six lanes, with three in each direction. During the weekday morning peak period, The Port Authority of New York and New Jersey (PANYNJ) operates a 2.5 miles exclusive bus lane (XBL) for traffic bound for Manhattan. The PANYNJ is considering creating, from existing lanes, a second XBL with the option for passenger vehicles to use it in return for an additional toll, in effect turning it into a high occupancy toll (HOT) lane. Such an approach to increase capacity and reduce congestion is unique nationally and this study looks at drivers'' choices between using standard lanes, paying extra to drive on a HOT lane (the new XBL lane), switch to earlier or later departure times, or change their mode of travel. The analysis shows significant differences in the valuation of travel time savings between different population groups and also different departure time periods. The models also reveal a reluctance to change to other crossings, accept changes in departure time or switch to alternative modes. [Copyright &y& Elsevier]

A significant portion of nonrecurrent freeway congestion is caused by incidents. This nonrecurrent congestion negatively impacts safety and mobility. It produces enormous travel delay and results in secondary incidents, which cause additional delay and injury. The simulation-based secondary incident filtering (SBSIF) method is proposed for identifying secondary incidents from archived incident data. The proposed methodology is computationally efficient and overcomes deficiencies of existing techniques. It is evaluated on incident data from a 6-month period along a segment of I-287 in the New York state. Results of the evaluation show that the SBSIF method has a significantly reduced misclassification rate (e.g., a reduction of 58 percentage points and greater) as compared with static methods commonly used in practice, despite that, it requires comparable computational effort. [ABSTRACT FROM AUTHOR]

Planning historians seldom examine freight infrastructure proposals as potential interventions into the political economic trajectories of materially productive regions. Combining interpretive approaches from planning history and economic geography, this article discusses one such proposal, the Joint Report with Comprehensive Plan and Recommendations, proposed in 1920, by the New York/New Jersey Port and Harbor Development Commission, as a response to the traffic problems generated by New York City's innovative, small-batch manufacturing economy. Pointing out some of the limitations of the established explanations for the dramatic decline of this industrial center, the article argues that the nondevelopment of any local freight infrastructure similar to that proposed by the Joint Report closed off the continued development of the city's materially productive economy as a historical possibility. [ABSTRACT FROM AUTHOR]

Abstract: We developed regression equations to predict fine particulate matter (PM2. 5) at air monitoring locations in the New York City region using data on nearby traffic and land use patterns. Three-year averages (1999–2001) of PM2. 5 at US Environmental Protection Agency (EPA) monitors in the 28 counties including and surrounding New York City were calculated using daily data from the EPA''s Air Quality Subsystem. As the secondary contribution to PM2. 5 concentrations is lowest in the winter, we also calculated and modeled average winter 2000 PM2. 5 to conduct a preliminary evaluation of model sensitivity to source contribution. Candidate predictor variables included traffic, land use, census and emissions data from local, state and national sources and were tabulated for a series of circular buffer regions at varying distances around the monitors using a geographic information system. In total, more than 25 variables at 5 different buffer distances were considered for inclusion in the model. Before evaluating the variables we removed several samples from the modeling for validation. For comparison and validation purposes we computed both a model using data for the full 28-county region as well as a more urbanized 9-county region. We found that traffic within a buffer of 300 or 500m explains the greatest proportion of variance (37–44%) in all 3 models. Measures of urbanization, specifically population density, explain a significant amount of the residual variation (7–18%) after including a traffic variable. Finally, a measure of industrial land use further improves the 28-county and 9-county models based on the 3-yr annual averages, explaining an additional 4% and 11% of the variation, respectively, while vegetative land use improves the winter model explaining an additional 6%. The final models predicted well at validation locations. In total, the final land use regression models explain between 61% and 64% of the variation in PM2. 5. [Copyright &y& Elsevier]

This article documents trends in travel and modal-split for trips into the Manhattan, New York City central business district (CBD) since World War II, and identifies factors that may have affected overall travel demand as well as the choice between the automobile and mass transit. The data presented indicate that the number of motor vehicles entering the Manhattan CBD has roughly doubled since World War II. The auto's modal-split share of trips into Manhattan also increased considerably. It appears that public policy has failed to keep auto use in Manhattan under control. Thus, there has been virtually no progress in mitigating the severe problems of congestion and pollution.