Drop-off Locations Research Articles

Throughput Capacity Comparison for Airport Pickup and Dropoff Facilities

An airport serves as an interface between ground and air transportation; the efficient processing of ground transportation arrivals and departures is, therefore, an important part of airport operations. At many airports, the current pickup and dropoff locations for taxis and other passenger cars are along the terminal curb or in existing parking facilities, and many of these pickup and dropoff facilities suffer from excessive congestion. In addition, in recent years there has been growth in the use of on-demand ground transportation to and from airports, with the increased adoption of ride-hailing services, resulting in increased congestion. Since most airports are severely space constrained, there is a need to consider pickup and dropoff facilities that are more efficient (in vehicle throughput per unit space) than terminal curbs. In this paper we compare the throughput capacities of different pickup and dropoff facility layouts. We take into account the effect of the facility layout and operational rules on conflicts between the movements of different vehicles, the resulting delays in the movements of vehicles, as well as the spatial requirements of different layouts. We demonstrate the impact of mean service times, variability in service times and vehicle movement times, and operational rules on the relative throughput capacities of different facility layouts.

Reinforcement-Learning-Based Route Generation for Heavy-Traffic Autonomous Mobile Robot Systems

Autonomous mobile robots (AMRs) are increasingly used in modern intralogistics systems as complexity and performance requirements become more stringent. One way to increase performance is to improve the operation and cooperation of multiple robots in their shared environment. The paper addresses these problems with a method for off-line route planning and on-line route execution. In the proposed approach, pre-computation of routes for frequent pick-up and drop-off locations limits the movements of AMRs to avoid conflict situations between them. The paper proposes a reinforcement learning approach where an agent builds the routes on a given layout while being rewarded according to different criteria based on the desired characteristics of the system. The results show that the proposed approach performs better in terms of throughput and reliability than the commonly used shortest-path-based approach for a large number of AMRs operating in the system. The use of the proposed approach is recommended when the need for high throughput requires the operation of a relatively large number of AMRs in relation to the size of the space in which the robots operate.

Discovering Intra-Urban Population Movement Pattern Using Taxis’ Origin and Destination Data and Modeling the Parameters Affecting Population Distribution

GPS-equipped vehicles are an effective approach for acquiring urban population movement patterns. Attempts have been made in the present study in order to identify the population displacement pattern of the study region using taxis’ origin and destination data, and then model the parameters affecting the population displacement pattern and provide an ultimate model in order to predict pick-up and drop-off locations. In this way, the passenger pick-up and drop-off locations have been identified in order to obtain the population movement pattern. In this study, Moran’s I index was used to measure the spatial autocorrelation, and hot spot analysis was used to analyze spatial patterns of pick-up and drop-off locations. Effective parameters modeling was performed using the Poisson regression. The results of the spatiotemporal distribution map for pick-up and drop-off locations indicated a similarity in patterns and equal results for some locations. Results also indicated different features of spatial distribution during different hours of the day. Spatial autocorrelation analysis results indicated a low probability of randomness in the general spatial distribution of the locations. The result of modeling the parameters shows the positive effect of the parameters on the pattern of population movement, and according to the p-value of 0.000, Poisson regression is significant for the pick-up and drop-off locations. The modeling results also highlighted the importance of movement patterns in recognizing urban hot spots, which is valuable for policymakers and urban planners.

Access to Secure Ballot Drop-off Locations in Texas

Safe access to voting during the Covid-19 pandemic was a concern across the US. In Texas, a push for mail-in voting during the November 2020 presidential election was significantly scaled back when the governor restricted each county to one secure ballot drop-off location. We evaluate the effect of this change, finding that it increased travel times to drop-off locations for drivers and public transit users by 18 and 32 minutes, respectively, in the most affected county. When evaluated across the state, the policy had a disproportionate effect on eligible Asian, Black, and Latinx voters.

Contract-based Inter-user Usage Coordination in Free-floating Car Sharing

We propose a novel distributed user-car matching method based on a contract between users to mitigate the imbalance problem between vehicle distribution and demand in free-floating car sharing. Previous regulation methods involved an incentive system based on the predictions of origin-destination (OD) demand obtained from past usage history. However, the difficulty these methods have in obtaining accurate data limits their applicability. To overcome this drawback, we introduce contract-based coordination among drop-off and pick-up users in which an auction is conducted for drop-off users' intended drop-off locations. We theoretically analyze the proposed method regarding the upper bound of its efficiency. We also compare it with a baseline method and non-regulation scenario on a free-floating car-sharing simulator. The experimental results show that the proposed method achieves a higher social surplus than the existing method.

Dynamic pricing of free-floating carsharing networks with sensitivity to travellers’ attitudes towards risk

Free-floating carsharing (FFCS) systems are characterised by volatile fleet distribution as well as customers’ heterogeneous price sensitivity and spatiotemporal flexibility. There is thus an opportunity for operators to employ dynamic pricing to manage various aspects of fleet allocation: which customer is provided which vehicle, at what time and price, and the agreed pick-up and drop-off location. While there are emerging examples of dynamic pricing in FFCS, there is as yet no general framework for the interaction of consumer and operator behaviours in this context, most particularly consumer response to the inherent risks and uncertainties in the journey characteristics noted above. In this study, we propose a choice-based framework for modelling the supply/demand interaction, drawing on behavioural models of decision-making in risky choice contexts and empirical stated-choice data of user preferences in a dynamically priced FFCS market. In addition to the ‘spot market’ mechanism of dynamic pricing, the proposed framework is capable of evaluating operator strategies of allowing (at an agreed price) customers to make guaranteed advance reservations. We demonstrate that this approach allows the system operator to set an optimal pricing strategy regardless of whether user risk preferences are risk-seeking or risk-averse. We also demonstrate the applicability of the proposed framework when the operator seeks to maximise revenue (as with a private operator) vs social welfare (as with a public operator). In the case study which employs empirical user preferences, we show that users’ risk preferences have a relatively small impact on revenue, however the impacts are much larger if there is a mismatch between users’ actual risk preferences and the system operator’s assumptions regarding users’ risk preferences.

Inferring the Purposes of using Ride-Hailing Services through Data Fusion of Trip Trajectories, Secondary Travel Surveys, and Land Use Data

This paper presents a data fusion methodology for inferring trip purposes from GPS trajectories of ride-hailing services in Toronto. The methodology has a discrete choice model at its core that predicts the most probable purpose distributions using only basic trip-related information such as approximate pick-up and drop-off locations, trip start times, and land use characteristics around the origins and destinations. The choice model is estimated using revealed trip purpose data from a small-sample travel survey augmented by land use information from an enhanced point of interest database and the census. The methodology is applied to the trajectories of commercial ride-hailing trips made in Toronto between September 2016 and September 2018. For the core choice model, multinomial, nested, and mixed multinomial logit models are compared. Validation of the inferred trip purposes using the trip purpose proportions from another independent survey (not used in choice model estimation) reveal that the multinomial logit model can infer ride-hailing trip purpose distribution with reasonable accuracy. The inferred purpose distribution explains the nature of ride-hailing trips and provides important context of travel demand generated by the services. The results indicate that although ride-hailing services are mostly used for discretionary activities, they also play important roles in daily commuter travel. A quarter of the total weekday ride-hailing trips were made for work- and school-related activities. With increasing ridership, these services may start influencing conventional travel modes and thereby adversely affect the level of traffic congestion and transit ridership in the city.

A real-time video surveillance system for traffic pre-events detection

In this study, a conceptual framework is proposed for the development of a video surveillance-based system for improving road safety. Based on the framework, a set of algorithms are developed which are capable of detecting various traffic pre-events from traffic videos, such as speed violation, one-way traffic, overtaking, illegal parking, and wrong drop-off location of passengers. After detecting the pre-events, an alarm will be automatically generated in the control room which helps to take precautionary measures to avoid any potential mishap on road, thereby, improving the road safety. In previous studies, a single system can handle either one or two pre-events. Whereas, in our present study, five anomalies can be detected in a single system using five different algorithms. Our study further contributes to the detection of “wrong drop-off location of passengers”. The effectiveness of the developed algorithms is demonstrated over 132 traffic videos acquired from an integrated plant in India. Some additional comparative studies for overtaking and illegal parking are done using two benchmark datasets, namely ‘CamSeq01’ and ‘ISLab-PVD’. Through an extensive study, it can be concluded that our developed algorithms are superior to some state-of-the-art algorithms in the detection of pre-events on road.

Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

A Study of Policy Implementation and Community Participation in the Municipal Solid Waste Management in the Philippines

Municipal solid waste (MSW) management has become one of the most pressing environmental concerns of the Philippines at present. Several measures have been implemented to circumvent this issue, including waste management policies stipulated in the Ecological Solid Waste Management Act of 2001. However, the implementation of these policies even at the barangay level has always been a challenge. Hence, this study assessed the compliance of selected barangays in Cebu City, Philippines, specifically to the integrated solid waste management plan based on the 3R’s (reduce, reuse, and recycle). A descriptive quantitative method was utilized in this study. There were 1523 residents and 30 Barangay Environment Officials identified as respondents based on Slovin’s sampling method at a 95% confidence level. The results revealed that there is less extent of compliance in almost all of the SWM policies such as segregation, composting, recycling, incentives, and public information. There is a moderate extent of compliance in terms of collection and transport of solid waste and enforcement of penalties and fines. However, there is non-compliance in terms of facilities for final disposal. Furthermore, the results suggested that effective measures for recycling and composting should be undertaken to encourage higher participation among residents of the barangay. The presence of effective, functional, and marketable materials resource facilities and convenient drop-off locations for recyclable materials ensure final sorting according to its type for composting and recycling.

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.

Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study

Most existing shared automated mobility (SAM) services assume the door-to-door manner, i.e., the pickup and drop-off (PUDO) locations are the places requested by the customers (or demand-side). While some mobility services offer more affordable riding costs in exchange for a little walking effort from customers, their rationales and induced impacts (in terms of mobility and sustainability) from the system perspective are not clear. This study proposes a demand-side cooperative shared automated mobility (DC-SAM) service framework, aiming to fill this knowledge gap and to assess the mobility and sustainability impacts. The optimal ride matching problem is formulated and solved in an online manner through a micro-simulation model, Simulation of Urban Mobility (SUMO). The objective is to maximize the profit (considering both the revenue and cost) of the proposed SAM service, considering the constraints in seat capacities of shared automated vehicles (SAVs) and comfortable walking distance from the perspective of customers. A case study on a portion of a New York City (NYC) network with a pre-defined fleet size demonstrated the efficacy and promise of the proposed system. The results show that the proposed DC-SAM service can not only significantly reduce the SAV’s operating costs in terms of vehicle-miles traveled (VMT), vehicle-hours traveled (VHT), and vehicle energy consumption (VEC) by up to 53, 46 and 51%, respectively, but can also considerably improve the customer service by 30 and 56%, with regard to customer waiting time (CWT) and trip detour factor (TDF), compared to a heuristic service model. In addition, the demand-side cooperation strategy can bring about additional system-wide mobility and sustainability benefits in the range of 4–10%.

Optimum design of composite structures with ply drop-offs using response surface methodology

PurposeMany studies only take into account the ply stacking sequence as the design variable to determine the optimal ply drop-off location; however, it is necessary to optimize other parameters that have a direct influence on the ply drop-off site such as which plies should be dropped and in which longitudinal direction. That way, the purpose of this study is to find the most significant design variables relative to the drop-off location considering the transversal and longitudinal positions, seeking to achieve the optimal combination of ply drop-off locations that provides excellent performance for the laminate plate.Design/methodology/approachThis study aims to determine the optimal drop-off location in a laminate plate using the finite element method and an approach statistical with design of experiments (DOE).FindingsThe optimization strategy using DOE revealed to be satisfactory for analyzing laminate structures with ply drop-offs, demonstrating that not all design factors influence the response variability. The failure criterion response variable revealed a poor fit, with an adjusted coefficient of determination lower than 60%, thus demonstrating that the response did not vary with the ply drop-off location. Already the strain and natural frequency response variables presented high significance. Finally, the optimization strategy revealed that the optimal drop-off location that minimizes the strain and maximizes the natural frequency is the ply drop-off located of the end plate.Originality/valueIt was also noted that many researchers prefer evolutionary algorithms for optimizing composite structures with ply drop-offs, being scarce to the literature studies involving optimization strategies using response surface methodology. In addition, many studies only take into account the ply stacking sequence as the design variable to determine the optimal ply drop-off location; however, in this study, the authors investigated other important parameters that have direct influence on the ply drop-off site such as which plies should be dropped and in which longitudinal direction.

Demand Prediction of Ride-Hailing Pick-Up Location Using Ensemble Learning Methods

Ride-hailing and carpooling platforms have become a popular way to move around in urban cities. Based on the principle of matching riders with drivers, with Uber, Lyft and Didi having the largest market share. The challenge remains being able to optimally match rider demand with driver supply, reducing congestion and emissions associated with Vehicle clustering, deadheading, ultimately leading to surge pricing where providers raise the price of the trip in order to attract drivers into such zones. This sudden spike in rates is seen by many riders as disincentive on the service provided. In this paper, data mining techniques are applied to ultimately develop an ensemble learning model based on historical data from City of Chicago Transport provider’s dataset. The objective is to develop a dynamic model capable of predicting rider drop-off location using pick-up location data then subsequently using drop-off location data to predict pick-up points for effective driver deployment under multiple scenarios of privacy and information. Results show neural network algorithms perform best in generalizing pick-up and drop-off points when given only starting point information. Ensemble learning methods, Adaboost and Random forest algorithm are able to predict both drop-off and pick-up points with a MAE of one (1) community area knowing rider pick-up point and Census Tract information only and in reverse predict potential pick-up points using the Drop-off point as the new starting point.

Preserving personalized location privacy in ride-hailing service

Ride-hailing service has become a popular means of transportation due to its convenience and low cost. However, it also raises privacy concerns. Since riders' mobility information including the pick-up and drop-off location is tracked, the service provider can infer sensitive information about the riders such as where they live and work. To address these concerns, we propose location privacy preserving techniques that efficiently match riders and drivers while preserving riders' location privacy. We first propose a baseline solution that allows a rider to select the driver who is the closest to his pick-up location. However, with some side information, the service provider can launch location inference attacks. To overcome these attacks, we propose an enhanced scheme that allows a rider to specify his privacy preference. Novel techniques are designed to preserve rider's personalized privacy with limited loss of matching accuracy. Through trace-driven simulations, we compare our enhanced privacy preserving solution to existing work. Evaluation results show that our solution provides much better ride matching results that are close to the optimal solution, while preserving personalized location privacy for riders.

Texas Supreme Court 2020 Election Litigation: List of Cases with Hyperlinks to Dockets and Opinions

The 2020 elections were the most heavily litigated in U.S. history. Texas is no exception. This paper presents comprehensive data for election law cases filed in the Supreme Court of Texas (SCOTX), whether they resulted in an opinion or not. As of Nov. 9, 2020, the Texas Supreme Court has issued at total of 17 opinions in 31 election law cases, including concurrences and dissents. One member of the nine-member court, Justice John Devine, was a particularly active dissenter, with both separate opinions and with dissents merely noted on the docket. Some of the election-related cases were highly contentious and attracted ample media attention, as well as a total of 50 amicus curiae briefs and letters. The array of legal disputes before the Texas Supreme Court encompassed wrangling over ballot access by candidates (including those of minor parties), a dispute with the City of Houston over cancellation of the GOP’s in-person convention, the legal uncertainty regarding voters’ eligibility to vote by mail in times of pandemic, and efforts to squelch local election officials’ innovations in the management of elections, such as promotion of absentee voting as a pandemic mitigation strategy, drop-off locations for mail ballots, and drive-thru voting in Harris County. With respect to voting rights, the all-Republican Court ruled in a manner to restrict voting, consonant with President Trump’s rhetoric about voter fraud as also echoed by leading GOP politicians in Texas. The Republican Attorney General and is his army of litigators performed a leading role in that effort, both in public communication, and in state and federal courts. However, the Texas Supreme Court did not always favor Republican petitioners, rejecting requests for relief on various grounds, including lack of jurisdiction in one case and unjustified delays in bringing their complaints to the court. Not all of the controversies presented to the Texas high court led to decisions on the merits. Noteworthy is the Court’s rejection of Republican challenges to drive-thru voting in Harris County without ruling on whether the practice conforms with the Texas Election Code, and without resolving whether the validity of more than 120,000 ballots already cast by drive-thru voters was affected. Four sitting members of the court, including Chief Justice Nathan Hecht, were themselves on the ballots in 2020, and in the end comfortably won reelection even though Texas was said to be “in play” in the lead-up to early voting.

Testing Curbside Management Strategies to Mitigate the Impacts of Ridesourcing Services on Traffic

Increased use of ridesourcing leads to increased pick-up and drop-off activity. This may slow traffic or cause delays as vehicles increase curb use, conduct pick-up and drop-off activity directly in the travel lane, or slow to find and connect with passengers. How should cities respond to this change in an effort to keep travel lanes operating smoothly and efficiently? This research evaluates two strategies in Seattle, WA, in an area where large numbers of workers commute using ridesourcing services: (i) a change of curb allocation from paid parking to passenger load zone (PLZ), and (ii) a geofencing approach by transportation network companies (TNCs) which directs their drivers and passengers to designated pick-up and drop-off locations on a block. An array of data on street and curb activity along three study blockfaces was collected, using video and sensor technology as well as in-person observations. Data were collected in three phases: (i) the baseline, (ii) after the new PLZs were added, expanding total PLZ curb length from 20 ft to 274 ft, and (iii) after geofencing was added to the expanded PLZs. The added PLZs were open to any passenger vehicle (not just TNC vehicles), weekdays 7:00–10:00 a.m. and 2:00–7:00 p.m. The results showed that the increased PLZ allocation and geofencing strategy reduced the number of pick-ups/drop-offs in the travel lane, reduced dwell times, increased curb use compliance, and increased TNC passenger satisfaction. The two strategies, however, had no observable effect on travel speeds or traffic safety in the selected study area.

Exploring ridesourcing trip patterns by fusing multi-source data: A big data approach

Ridesourcing service has been playing an increasingly important role in urban travel, which it can be regard as a promising path towards urban sustainability because of its nature of “sharing”. This paper investigated the travel behavior of ridesourcing users, by considering pick-up and drop-off locations fusing Didi ridesourcing data and online Point of Interest (POI) data based on the Latent Dirichlet Allocation (LDA) model. A case study was conducted in Chengdu, an Asian city. Firstly, the study area was tessellated with hexagons and then clustered into five hexagon types based on POI data. Then researchers analyzed information on spatial distribution of POI data in five hexagon types and temporal distribution of boarding and alighting ridership for five hexagon types. The validation results indicated that the expression “Nine to Ten” represents the mode of life for people in Chengdu. Ridesourcing is not yet being used as a commuter tool for most people. LDA model results also verified that many people work overtime in the evenings and especially on Saturday, even though Chengdu is often thought of as a “slow city”. The study will be useful for practitioners and government to implement effective policies about multi-modal transportation.

Seyahat Süresi Değişkenliğinin ve Güvenilirliğinin İstatistiksel Analizi

Şehir içi ulaşım ağlarında bir noktadan başka bir noktaya yapılan yolculukların seyahat sürelerinin istatistiksel olarak modellenmesi ve bunların güvenirliğinin analiz edilmesi büyük önem arz etmektedir. Gerçekleştirilen kimi çalışmalarda seyahat süresini modellemek için normal dağılım gibi teorik olarak uygun olmayan dağılımlar tercih edilmiştir. Bunun yanında birçok çalışma haftanın günleri hafta içi ve hafta sonu olmak üzere iki farklı sınıfta değerlendirmektedir. Gelişen sensör teknolojileri ile elde edilen büyük ulaşım verisi üzerinden seyahat sürelerinin istatiksel dağılımı ve güvenirliği gibi şehir hayatına etki eden faktörler analiz edilebilmektedir. Bu çalışmada New York şehrine ait açık ulaşım verisi analiz edilerek, birçok çalışmanın varsaydığı hafta içi-hafta sonu ayrımının gerçekçi olmayabileceği ve bir gün içindeki farklı zaman dilimlerinde istatistiksel sonuçların farklı çıkabileceği tespit edilmiştir. John F. Kennedy ve LaGuardia havalimanları arasında 07:00-19:00 arasında gerçekleşen taksi yolculuklarının seyahat süresi değişkenliği ve dağılımları incelendiğinde, seyahat sürelerinin Log-Lojistik (3P) dağılımına uyduğu tespit edilmiştir. Bu dağılım Salı ve Çarşamba günleri baskın dağılım değildir ve bu günlerde seyahat süresi güvenilirliğinin düşük olduğu zaman dilimlerinin bulunduğu tespit edilmiştir.

Free-floating carsharing users’ willingness-to-pay/accept for logistics management mechanisms

The spatio-temporal flexibility of free-floating carsharing (FFCS) fleets leads to vehicle stock imbalances across the network. One set of strategies for managing fleet distribution involves incentivising users to participate in relocating the vehicles. The objective of this study is to establish FFCS customers’ preferences for each of four incentivisation mechanisms: 1) vehicle delivery, 2) paid relocation, and 3–4) incentivisation for alternate vehicle pick-up and drop-off locations. Survey data (n = 311; collected Sept. 2017) from FFCS users in Vancouver and Washington D.C. are employed to quantify willingness-to-pay/accept (WTP/WTA) for these mechanisms. We find that a majority of respondents report positive attitudes (“definitely” or “possibly” willing to use) toward each of the four incentivisation mechanisms, with alternate drop-off the highest (57%) and paid relocation the lowest (40%). Regression analysis finds that user experiences using FFCS are generally stronger predictors of WTP/WTA than socio-demographic features, with (intuitively) the frequency of FFCS unavailability the strongest predictor. Age is the strongest socio-demographic predictor, with the WTP for vehicle delivery increasing and the size of required incentives for alternate pick-up/drop-off locations decreasing with age. Finally, we performed k-means cluster analysis of respondents based on the times-of-week that they report experiencing difficulty finding an available FFCS vehicle, and identified four distinct segments of users. However, we found generally weak relationships between WTP/WTA and the specific time-of-week periods that unavailability is experienced.