Trip Count Research Articles

Side-channel Elimination via Partial Control-flow Linearization

Partial control-flow linearization is a code transformation conceived to maximize work performed in vectorized programs. In this article, we find a new service for it. We show that partial control-flow linearization protects programs against timing attacks. This transformation is sound: Given an instance of its public inputs, the partially linearized program always runs the same sequence of instructions, regardless of secret inputs. Incidentally, if the original program is publicly safe, then accesses to the data cache will be data oblivious in the transformed code. The transformation is optimal: Every branch that depends on some secret data is linearized; no branch that depends on only public data is linearized. Therefore, the transformation preserves loops that depend exclusively on public information. If every branch that leaves a loop depends on secret data, then the transformed program will not terminate. Our transformation extends previous work in non-trivial ways. It handles C constructs such as “goto,” “break,” “switch,” and “continue,” which are absent in the FaCT domain-specific language (2018). Like Constantine (2021), our transformation ensures operation invariance but without requiring profiling information. Additionally, in contrast to SC-Eliminator (2018) and Lif (2021), it handles programs containing loops whose trip count is not known at compilation time.

Impacts of COVID-19 on bike-sharing usages in Seoul, South Korea

The COVID-19 pandemic and social distancing restrictions have had a significant impact on urban mobility. As micro mobility offers less contact with other people, docked or dockless e-scooters and bike-sharing have emerged as alternative urban mobility solutions. However, little empirical research has been conducted to investigate how COVID-19 might affect micro mobility usage, especially in a major Asian city. This research aims to study how COVID-19 and other related factors have affected bike-sharing ridership in Seoul, South Korea. Using detailed urban telecommunication data, this study explored the spatial-temporal patterns of a docked bike-sharing system in Seoul. Stepwise negative binomial panel regressions were conducted to find out how COVID-19 and various built environments might affect bike-sharing ridership in the city. Our results showed that open space areas and green infrastructure had statistically significant positive impacts on bike-sharing usage. Compared to registered population factors, real-time telecommunication floating population had a significant positive relationship with both bike trip count and trip duration. The model showed that telecommunication floating population has a significant positive impact on bike-sharing trip counts and trip duration. These findings could offer useful guidelines for emerging shared mobility planning during and after the COVID-19 pandemic.

The Chance of Getting Struck by a Car on an e-Scooter is Twice as High at Night

Nighttime crash risk is higher across all modes of transportation. Despite regulatory pressure to intervene in nighttime e-scooter riding, there is limited understanding of the number of crashes and crash rates by daytime and nighttime. Motor vehicle-involved crashes are most dangerous. This study combined 82 police crash reports with data from 3.1 million shared e-scooter trips in Nashville, TN from September 2018 to January 2022 to evaluate the time-of-day and seasonal crash patterns, controlling for exposure. E-scooter crashes, with cars at least, are more likely to occur during the nighttime, as indicated by crash rates estimated from trip count as an exposure variable.

How Long Should GPS Recording Lengths Be to Capture the Community Mobility of An Older Clinical Population? A Parkinson's Example.

Wearable global position system (GPS) technology can help those working with older populations and people living with movement disorders monitor and maintain their mobility level. Health research using GPS often employs inconsistent recording lengths due to the lack of a standard minimum GPS recording length for a clinical context. Our work aimed to recommend a GPS recording length for an older clinical population. Over 14 days, 70 older adults with Parkinson’s disease wore the wireless inertial motion unit with GPS (WIMU-GPS) during waking hours to capture daily “time outside”, “trip count”, “hotspots count” and “area size travelled”. The longest recording length accounting for weekend and weekdays was ≥7 days of ≥800 daily minutes of data (14 participants with 156, 483.9 min recorded). We compared the error rate generated when using data based on recording lengths shorter than this sample. The smallest percentage errors were observed across all outcomes, except “hotspots count”, with daily recordings ≥500 min (8.3 h). Eight recording days will capture mobility variability throughout days of the week. This study adds empirical evidence to the sensor literature on the required minimum duration of GPS recording.

Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems

Dockless bikesharing (DBS) has been considered as a solution to the first and last mile problem of metro connectivity. Leveraging data covering all DBS programs in Shanghai, China, this study evaluated bike-and-ride (BnR) activities in DBS-metro systems via four metrics: BnR trip count, BnR rate, shared-bike utilization rate, and catchment size (85th percentile transfer distance). A set of generalized additive models considering marginal nonlinear interactions was fitted to examine associations between the four metrics and external environment, including land use, socio-demographics, roadway designs, transportation facilities, metro station features, and DBS operator features. Different buffer sizes measured by network distance were tested to check model robustness and find optimal buffers. Results showed that: 1) metro stations near the city center exhibited greater BnR trip count, higher BnR rate, lower shared-bike utilization rate, and smaller catchment size; 2) proportion of public and residential land suggested positive relationships with BnR trip count but lose their significances after offsetting metro ridership; 3) numbers of colleges, shopping malls, and carsharing stations presented positive relationships with both BnR trip count and BnR rate; 4) land use mix was significantly positively associated with BnR trip count only when buffer size was larger than 1.5 km; 5) regions with higher population density went from less BnR activities in the city center to more BnR activities in the suburbs; 6) Large DBS operators outperformed small ones in BnR trip count but not in bike utilization rate. Taken together, this study uncovers a spatially disproportionate and supply-demand unbalanced distribution of DBS resources, which could attenuate the efficiency and attractiveness of using DBS to BnR. DBS operators and local governments should evaluate DBS systems from multiple perspectives to avoid an oversupplied and over-competing market.

How Does Ride-Hailing Influence Individual Mode Choice? An Examination Using Longitudinal Trip Data from the Seattle Region.

This study investigates the impacts of ride-hailing, which we define as mobility services consisting of both conventional taxis and app-based services offered by transportation network companies, on individual mode choice. We examine whether ride-hailing substitutes for or complements travel by driving, public transit, or walking and biking. The study overcomes some of the limitations of convenience samples or cross-sectional surveys used in past research by employing a longitudinal dataset of individual travel behavior and socio-demographic information. The data include three waves of travel log data collected between 2012 to 2018 in transit-rich areas of the Seattle region. We conducted individual-level panel data modeling, estimating independently pooled models and fixed-effect models of average daily trip count and duration for each mode, while controlling for various factors that affect travel behavior. The results provide evidence of substitution effects of ride-hailing on driving. We found that cross-sectionally, participants who used more ride-hailing tended to drive less, and that longitudinally, an increase in ride-hailing usage was associated with fewer driving trips. No significant associations were found between ride-hailing and public transit usage or walking and biking. Based on detailed travel data of a large population in a major US metropolitan area, the study highlights the value of collecting and analyzing longitudinal data to understand the impacts of new mobility services.

Automated monitoring and analyses of honey bee pollen foraging behavior using a deep learning-based imaging system

Pollen foraging efficiency provides vital information for the behavioral research on honey bees. The pollen production of beehives can be measured by manually weighing the pollen collected from pollen traps. For long-term pollen foraging monitoring, this approach is both inefficient and laborious. This study presents an efficient method for automatically monitoring the pollen foraging behavior and environmental conditions through an embedded imaging system. The imaging system uses an off-the-shelf camera installed at the beehive entrance to acquire video streams that are processed using the developed image processing algorithm. A lightweight real-time object detection and deep learning-based classification model, supported by an object tracking algorithm, was trained for counting and recognizing honey bee into pollen or non-pollen bearing class. The F1-score was 0.94 for pollen and non-pollen bearing honey bee recognition, and the precision and recall values were 0.91 and 0.99, respectively. For foraging trip counting algorithm, the mean average percent errors of the pollen bearing honey bee count and the total incoming honey bee count were 8.45 ± 2.72% and 10.55 ± 2.10%, respectively. An experiment was performed to test the performance of the imaging system in continuous monitoring of honey bee pollen foraging behavior as well as to investigate the effect caused by weather factors. The incoming and outgoing honey bee count were recorded and used to calculate indices based on the hourly and daily recorded counts for further analyses. The experimental results and analyses revealed that the daily pollen foraging trip ratio was 24.5 ± 3.5%; a single beehive collected about 49.1 ± 11.0 g of pollen per day. The pollen foraging trip count increased with increasing temperature and light intensity, and decreased with increasing relative humidity, rain level and wind speed. A significant reduction of pollen foraging activities was observed in heavy rainfall or gentle breeze conditions. This study not only quantitatively presents the effect of environmental factors on pollen foraging behavior, but also demonstrates the efficacy of the proposed imaging system. The automated imaging system can be applied as an efficient and reliable tool for researchers to gain deeper insights into honey bee foraging behavior, and help beekeepers achieve beehive management.

A Hierarchical Fuzzy-Based Correction Algorithm for the Neighboring Network Hit Problem

Most humans today have mobile phones. These devices are permanently collecting and storing behavior data of human society. Nevertheless, data processing has several challenges to be solved, especially if it is obtained from obsolete technologies. Old technologies like GSM and UMTS still account for almost half of all devices globally. The main problem in the data is known as neighboring network hit (NNH). An NNH occurs when a cellular device connects to a site further away than it corresponds to by network design, introducing an error in the spatio-temporal mobility analysis. The problems presented by the data are mitigated by eliminating erroneous data or diluting them statistically based on increasing the amount of data processed and the size of the study area. None of these solutions are effective if what is sought is to study mobility in small areas (e.g., Covid-19 pandemic). Elimination of complete records or traces in the time series generates deviations in subsequent analyses; this has a special impact on reduced spatial coverage studies. The present work is an evolution of the previous approach to NNH correction (NFA) and travel inference (TCA), based on binary logic. NFA and TCA combined deliver good travel counting results compared to government surveys (2.37 vs. 2.27, respectively). However, its main contribution is given by the increase in the precision of calculating the distances traveled (37% better than previous studies). In this document, we introduce FNFA and FTCA. Both algorithms are based on fuzzy logic and deliver even better results. We observed an improvement in the trip count (2.29, which represents 2.79% better than NFA). With FNFA and FTCA combined, we observe an average distance traveled difference of 9.2 km, which is 9.8% better than the previous NFA-TCA. Compared to the naive methods (without fixing the NNHs), the improvement rises from 28.8 to 19.6 km (46.9%). We use duly anonymized data from mobile devices from three major cities in Chile. We compare our results with previous works and Government’s Origin and Destination Surveys to evaluate the performance of our solution. This new approach, while improving our previous results, provides the advantages of a model better adapted to the diffuse condition of the problem variables and shows us a way to develop new models that represent open challenges in studies of urban mobility based on cellular data (e.g., travel mode inference).

Comparing the Temporal Determinants of Dockless Scooter-share and Station-based Bike-share in Washington, D.C.

Abstract Dockless, or free-floating mobility has gained unprecedented popularity in the last year, from being virtually non-existent in 2017 to facilitating over 38.5 million trips in 2018. Hitherto, few studies have analyzed dockless micromobility, and scooter-share particularly using big data. This paper analyzes and compares the determinants of dockless scooters-share (DSS) and of station-based bike-share (SBBS) rides in D.C. It made use of API data from dockless vendors and historical trip data from Capital Bikeshare from December 2018 to June 2019. Two variables were estimated: hourly number of trips and hourly median duration of trips. A negative-binomial regression model was performed at the hourly scale controlling for environmental and economic variables including weather-related data, gasoline prices, local events or disturbances, day of week, and time of day. Four groups were analyzed: all of micromobility combined and weighed, SBBS members, SBBS non-members, and DSS. Three important findings emerged: (1) Temporal use differences between the three user groups were found, but DSS users behave most similarly to SBBS non-members. (2) Weather is less of a disutility for DSS users than for SBBS users. We attribute this to the physical ease of using a scooter and to the convenience of ending a trip at the actual destination rather than a nearby docking station. (3) All micromobility user types are sensitive to changing gas prices, although DSS users appear slightly more sensitive both in terms of trip count and duration. Additionally, an analysis of the interaction between modes found a possible competition between DSS and SBBS non-members and a complementary relationship between DSS and SBBS members. We conclude that significant differences exist between the two modes, and combined with its sudden and rising popularity, micromobility and DSS in particular could have a major role in promoting a shift towards low-carbon mobility.

Willingness-to-pay for snowmobile recreation: travel cost method models with and without post-season resurvey of trip count

ABSTRACTAnnual willingness-to-pay (WTP) for snowmobile recreation in Idaho is estimated using the travel cost method (TCM). On-site snow conditions are important and erratic, thus we collect two measures of the annual trip count, an in-season survey of the expected count, and a post-season survey of the actual count. Two variants of the TCM model are estimated. Using the post-season actual trip count data and the ‘traditional’ TCM model, WTP increases from $41 to $91 per person per trip as the fraction of the wage rate that is used to value the opportunity cost of travel time is increased from 1/4 to one. Using the preferred short-run decision TCM model, WTP increases from $53 to $194 as the snowmobiler ratings of off-trail snow conditions vary from worst to best. WTP estimates using the in-season expected trip count data and the traditional TCM model are much higher (triple) than those found using the post-season actual trip count data, and the confidence intervals are much larger.

Modeling Trip Count Data with Excess Zeros for U.S. Saltwater Recreational Fishing

Modeling Trip Count Data with Excess Zeros for U.S. Saltwater Recreational Fishing

Identification of urban regions' functions in Chengdu, China, based on vehicle trajectory data.

Data about human trajectories has been widely used to study urban regions that are attractive to researchers and are considered to be hotspots. It is difficult, however, to quantify the function of urban regions based on the varieties of human behavior. In this research, we developed a clustering method to help discover the specific functions that exist within urban regions. This method applies the Gaussian Mixture Model (GMM) to classify regions’ inflow and trip count characteristics. It regroups these urban regions using the Pearson Correlation Coefficient (PCC) clustering method based on those typical characteristics. Using a large amount of vehicle trajectory data (approximately 1,500,000 data points) in the Chinese city of Chengdu, we demonstrate that the method can discriminate between urban functional regions, by comparing the proportion of surface objects within each region. This research shows that vehicle trajectory data in different functional urban regions possesses different time-series curves, while similar types of functional regions can be identified by these curves. Compared with remote sensing images and other statistical methods which can provide only static results, our research can provide a timely and effective approach to determine an urban region’s functions.

The impacts of built environment characteristics of rail station areas on household travel behavior

Transit-oriented development (TOD) has gained popularity worldwide as a sustainable form of urbanism by concentrating developments near a transit station so as to minimize auto-dependency and maximize ridership. Existing TOD studies, however, have limits in terms of small sample size and aggregate-level analysis. This study examines various travel outcomes – VMT, auto trips, transit trips, and walk trips – in rail-based station areas in eight U.S. metropolitan areas in order to understand the role of neighborhood built environment characteristics. Two-stage hurdle models handle excess zero values in trip count variables and multi-level models deal with three-level data structure – household within station areas within regions. The final models show that automobile use is associated with land-use diversity and street network design of a station area; transit use is strongly related to transit availability and land-use diversity; and walking is related to transit availability, land-use diversity, and street network design. The weakest influence among station-area environment factors is density. In sum, a TOD, a station area having a dense, mixed-use, walkable, and transit-friendly environment, motivates residents to walk more and take transit more while driving less.

Effect of weather on pedestrian trip count and duration: City-scale evaluations using mobile phone application data.

We examined the association between meteorological (weather) conditions in a given locale and pedestrian trips frequency and duration, through the use of locative digital data. These associations were determined for seasonality, urban microclimate, and commuting. We analyzed GPS data from a broadly available activity tracking mobile phone application that automatically recorded 247,814 trips from 5432 unique users in Boston and 257,697 trips from 8256 users in San Francisco over a 50-week period. Generally, we observed increased air temperature and the presence of light cloud cover had a positive association with hourly trip frequency in both cities, regardless of seasonality. Temperature and weather conditions generally showed greater associations with weekend and discretionary travel, than with weekday and required travel. Weather conditions had minimal association with the duration of the trip, once the trip was initiated. The observed associations in some cases differed between the two cities. Our study illustrates the opportunity that emerging technology presents to study active transportation, and exposes new methods to wider consideration in preventive medicine.

Predicting HPC parallel program performance based on LLVM compiler

Performance prediction of parallel program plays key roles in many areas, such as parallel system design, parallel program optimization, and parallel system procurement. Accurate and efficient performance prediction on large-scale parallel systems is a challenging problem. To solve this problem, we present an effective framework for performance prediction based on the LLVM compiler technique in this paper. We can predict the performance of a parallel program on a small amount of nodes of the target parallel system using this framework toned but not execute this parallel program on a corresponding full-scale parallel system. This framework predicts the performance of computation and communication components separately and combines the two predictions to achieve full program prediction. As for sequential computation, we first combine the static branch probability and loop trip count identification and propose a new instrumentation method to acquire the number of each instruction type. We then construct a test program to measure the average execution time of each instruction type. Finally, we utilize the pruning technique to convert a parallel program into a corresponding sequential program to predict the performance on only one node of the target parallel system. As for communication, we utilize the LogGP model to model point-to-point communication and the artificial neural network technique to model collective communication. We validate our approach by a set of experiments that predict the performance of NAS parallel benchmarks and CGPOP parallel application. Experimental results show that the proposed framework can accurately predict the execution time of parallel programs, and the average error rate of these programs is 10.86%.

Potential and methods for embedding dynamic offloading decisions into application code

A broad spectrum of applications can be accelerated by offloading computation intensive parts to reconfigurable hardware. However, to achieve speedups, the number of loop iterations (trip count) needs to be sufficiently large to amortize offloading overheads. Trip counts are frequently not known at compile time, but only at runtime just before entering a loop. Therefore, we propose to generate code for both the CPU and the coprocessor, and defer the offloading decision to the application runtime. We demonstrate how a toolflow, based on the LLVM compiler framework, can automatically embed dynamic offloading decisions into the application code. We perform in-depth static and dynamic analysis of popular benchmarks, which confirm the general potential of such an approach. We also propose to optimize the offloading process by decoupling the runtime decision from the loop execution (decision slack). The feasibility of our approach is demonstrated by a toolflow that automatically identifies suitable data-parallel loops and generates code for the FPGA coprocessor of a Convey HC-1. We evaluate the integrated toolflow with representative loops executed for different input data sizes.

Estimating bike-share trips using station level data

Bicycle sharing systems (BSS) have increased in number rapidly since 2007. The potential benefits of BSS, mainly sustainability, health and equity, have encouraged their adoption through support and promotion by mayors in Europe and North America alike. In most cases municipal governments desire their BSS to be successful and, with few exceptions, state them as being so. New technological improvements have dramatically simplified the use and enforcement of bicycle return, resulting in the widespread adoption of BSS. Unfortunately little evaluation of the effectiveness of differently distributed and managed BSS has taken place. Comparing BSS systems quantitatively is challenging due to the limited data made available. The metrics of success presented by municipalities are often too general or incomparable to others making relative evaluations of BSS success arduous. This paper presents multiple methodologies allowing the estimation of the number of daily trips, the most significant measure of BSS usage, based on data that is commonly available, the number of bicycles available at a station over time. Results provide model coefficients as well as trip count estimates for select cities. Of four spatial and temporal aggregate models the day level aggregation is found to be most effective for estimation. In addition to trip estimation this work provides a rigorous formalization of station level data and the ability to distinguish spatio-temporal rebalancing quantities as well as new characteristics of BSS station use.

Empirical examination of neighborhood context of individual travel behaviors

In recent years, recognition of the importance of neighborhood context has produced a growing body of geographic research. When making their activity–travel decisions, individuals are restricted in different ways. In particular, individual choice behavior is often influenced by a neighborhood environment and a built environment. This study using the 2010 household trip survey demonstrated the effectiveness of incorporating multilevel mechanisms in various contexts of activity–travel behavior by comparing with traditional models. The analysis shows that one individual's activity participation patterns with respect to mode choice, trip count, trip distance, and trip time, under a variety of spatio-temporal constraints, tend to be affected by shared characteristics of neighborhoods. The results also imply that neighborhood travel behaviors are significantly influenced by neighborhood characteristics requiring policy makers to consider not only individual characteristics but surrounding environment.

A case for a fast trip count predictor

The Trip Count of a loop determines how many iterations this loop performs. Predicting this value is important for several compiler optimizations, which yield greater benefits for large trip counts, and are either innocuous or detrimental for small ones. However, finding an exact prediction, in general, is an undecidable problem. Such problems are usually approached via methods which tend to be computationally expensive. In this paper we make a case for a cheap trip count prediction heuristic, which is O(1) on the size of the loop. We argue that our technique is useful to just-in-time compilers. If we predict that a loop will iterate for a long time, then we invoke the JIT compiler earlier. Even though straightforward, our observation is novel. We show how this idea speeds up JavaScript programs, by implementing it in Mozilla Firefox. We can apply our heuristic in 79.9% of the loops found in typical JavaScript benchmarks. For these loops, we obtain exact predictions in 91% of cases. We get similar results when analyzing the C programs of SPEC CPU 2006. A more elaborate technique, linear on the size of the loop, improves our O(1) technique only marginally. As a consequence of this work, we have been able to speed up several JavaScript programs by over 5%, reaching 24% of improvement in one benchmark.

SIMD defragmenter

Single-instruction multiple-data (SIMD) accelerators provide an energy-efficient platform to scale the performance of mobile systems while still retaining post-programmability. The central challenge is translating the parallel resources of the SIMD hardware into real application performance. In scientific applications, automatic vectorization techniques have proven quite effective at extracting large levels of data-level parallelism (DLP). However, vectorization is often much less effective for media applications due to low trip count loops, complex control flow, and non-uniform execution behavior. As a result, SIMD lanes remain idle due to insufficient DLP. To attack this problem, this paper proposes a new vectorization pass called SIMD Defragmenter to uncover hidden DLP that lurks below the surface in the form of instruction-level parallelism (ILP). The difficulty is managing the data packing/unpacking overhead that can easily exceed the benefits gained through SIMD execution. The SIMD degragmenter overcomes this problem by identifying groups of compatible instructions (subgraphs) that can be executed in parallel across the SIMD lanes. By SIMDizing in bulk at the subgraph level, packing/unpacking overhead is minimized. On a 16-lane SIMD processor, experimental results show that SIMD defragmentation achieves a mean 1.6x speedup over traditional loop vectorization and a 31% gain over prior research approaches for converting ILP to DLP.