Trip Production Research Articles

Investigation of crowdshipping delivery trip production with real-world data

Crowd-shipping (CS) is an innovative logistics service that occasional and professional couriers sign up for via an online platform to deliver packages upon requests by senders. Currently the demand and supply of CS is not yet well understood, largely due to the limited real-world data. This study aims to first fill this gap by analyzing the real-world CS data from the city of Atlanta, GA between April 2015 and August 2018. We first present an overview of the real-world CS data in three aspects: (1) the CS pricing scheme; (2) the CS spatial and temporal delivery patterns; and (3) comparison of preferences between the senders’ requests and the couriers’ bids. The analysis finds that the CS service has a clear price advantage over FedEx in the same-day and express service, as well as in the large, extra large, and huge size package delivery. The data analysis also reveals considerable discrepancies between senders’ and couriers’ preferences. We then compare two classes of the state-of-the-art Deep Learning (DL) methods in their ability to predict short-term CS delivery trip production. One class captures only the temporal features, namely the Long Short-term Memory Neural Network (LSTM), the Bidirectional Long Short-term Memory Neural Network (BDLSTM), and the Gated Recurrent Unit (GRU). The other class considers both spatial and temporal features, namely Convolutional Neural Network (CNN), CNN-LSTM, and ConvLSTM. The results show that ConvLSTM has overall the best predictive performance among the six DL methods considered, proving the importance of capturing both the spatial and temporal features of the delivery trip production data, as well as the convolutional nature of the spatial and temporal features in the data.

Improvement of Trip Attraction Model in Surabaya by Considering Geographical Weighting of City Centre Activity Function

It is challenging to have a trip attraction model that fits Surabaya's surveyed data due to unclear city centre structure. These include the centrum of concentric, corridors of sectoral, or several centres of multiple-nuclei structures. Also, the layout of residential areas has unconventional patterns. This is because the planned housing development area is wrongly inserted on kampong and sometimes lies in city centre. This paper examines the influence of single-centre districts, corridors, or multiple suburb centre structures on trip attraction. The analysis was conducted using origin-destination data from the household interview survey by The Transportation Board of Surabaya and several houses digitized from a relevant year's satellite image. The distance and position information was taken from the Google Earth application. The zonal analysis trip attraction model based on the sub-district zoning system was analysed using fixed trip production data and simulated independent variables. The independent variables included the zonal activity areas such as shops, offices, and industries in sub-district, while the dependent variables consisted of the straight distances from the sub-district to city centres. Several models were tested based on the dependent and independent variables. The results show that the combined zonal activity area and spatial variables have a stronger influence on zonal trip attraction than the conventional model using zonal labor and student variable, mainly based on the urban geographical pattern.

Incorporating spatial interactions in zero-inflated negative binomial models for freight trip generation

This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities.

Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling

Traditional approaches to travel behaviour modelling primarily rely on household travel survey data, which is expensive to collect, resulting in small sample sizes and infrequent updates. Furthermore, such data is prone to reporting errors which can lead to biased parameter estimates and subsequently incorrect predictions. On the other hand, mobile phone call detail records (CDRs), which report the timestamped locations of mobile communication events, have been successfully used in the context of generating travel patterns. However, due to their anonymous nature, such records have not been widely used in developing mathematical models establishing the relationship between the observed travel behaviour and influencing factors such as the attributes of the alternatives and the decision makers. In this paper, we propose a joint modelling framework that utilises the advantages offered by both travel survey data and low-cost CDR data to optimise the prediction capacity of traditional trip generation models. In this regard, we develop a model that jointly explains the reported trips for each individual in the household survey data and ensures that the aggregated zonal trip productions are close to those derived from CDR data. This framework is tested using data from Dhaka. Bangladesh consisting of household survey data (65,419 persons in 16,750 households), mobile phone CDR data (over 600 million records generated by 6.9 million users), and aggregate census data. The model results show that the proposed framework improves the spatial and temporal transferability of the joint models over the base model which relies on household travel survey data alone. This serves as a proof-of-concept that augmenting travel survey data with mobile phone data holds significant promise for the travel behaviour modelling community, not only by saving the cost of data collection, but also improving the prediction capability of the models.

Effects of Business Age and Size on Freight Demand: Decomposition Analysis of Indian Establishments

Freight studies have traditionally used business size measures such as employment and gross floor area as predictors of freight generation, giving very limited attention to the effects of an establishment’s age on freight demand. This study uses establishment-based freight survey data collected in seven cities of Kerala, India, to analyze the impacts of the age of an establishment on its freight demand. This is achieved by grouping the establishments into pre-specified classes with relatively homogeneous freight demand pattern. This classification is based on a data-driven a posteriori segmentation of industrial classes. These groups are further divided into sub-groups based on founding year of an establishment. As the business age increases, establishments grow in business size indicators and hence the changes in productivity can be either because of age or because of size or both. It is very important to separate the effects of these two indicators. The Blinder–Oaxaca decomposition method is used in this study to disentangle the effects of size and age on freight production and freight trip production. Age is found to be a fundamental driver of freight demand in younger establishments while the explanatory power of business size variables in explaining freight demand diminishes with age. The study findings illustrate the potential omitted variable bias that can occur when freight demand is estimated using business size indicators, without controlling for the business age differences.

Exploring spatial variation of bike sharing trip production and attraction: A study based on Chicago’s Divvy system

Bike sharing systems are adopted by many cities due to its contribution to energy saving and mitigation of traffic congestion. Understanding factors that influence bike sharing ridership and accurate estimation of ridership play an important role in designing the system. Previous studies assume the relationship between predicting variables and the response variable are the same across the study area. However, this assumption may not be true, since the study area is usually wide and thus the relationship between predicting variabels and the response variable may change across space. As a result, semi-parametric geographically weighted regression (S-GWR) model is used to explore the spatially varying relationship. S-GWR is an extension of the GWR model. While in GWR model, all predicting variables are local variables with spatially varying relationship with the response variable, S-GWR model allows predicting variables to be either global or local, which is closer to reality. We also extend previous studies by differenciating members and 24-h pass users, as well as data related to trip production and trip attraction. Results show that S-GWR models fit the data better and the relationship between some predicting variables and response variable are local while other relationships are global. Ridership of both members and 24-h users are positively related to number of employed residents nearby and capacity of the station, and negatively related to distance to central business area and percent of low-income workers living nearby. Number of employments is only significantly associated with trip attraction. Among them, the variable capacity is always a global variable, with higher capacity associated with higher ridership. As a result, S-GWR model could be used to estimate the ridership of stations for accurate prediction and spatially varying relationship between ridership and influencing factors should be considered when designing bike sharing system.

To Pool or Not to Pool: Equilibrium, Pricing and Regulation

We study a monopoly transportation network company (TNC) in an aggregate market that offers on-demand solo and pooling e-hail services, while competing with transit for passengers. The market equilibrium is established based on a spatial driver-passenger matching model that characterizes the passenger wait time for both solo and pooling rides. We prove, under mild conditions, this system always has an equilibrium solution. Built on the market equilibrium, three variants of pricing problems are analyzed and compared, namely, (i) profit maximization, (ii) profit maximization subject to regulatory constraints, and (iii) social welfare maximization subject to a revenue-neutral constraint. A comprehensive case study is constructed using TNC data collected in the city of Chicago. We found pooling is desirable when demand is high, but supply is scarce. However, its benefit diminishes quickly as the average en-route detour time (i.e., the difference between the average duration of solo and pooling trips) increases. Without regulations, a mixed strategy—providing both solo and pooling rides—not only achieves the highest profit and trip production in most scenarios, but also gains higher social welfare. The minimum wage policy can improve social welfare in the short term. However, in the long run, the TNC could react by limiting the size of the driver pool, and consequently, render the policy counterproductive, even pushing social welfare below the unregulated level. Moreover, by maintaining the supply and demand of ride-hail at an artificially high level, the minimum wage policy tends to exacerbate traffic congestion by depressing the use of collective modes (transit and pooling). A congestion tax policy that penalizes solo rides promotes pooling, but consistently harms social welfare. However, it promises to increase both social welfare and pooling ratio, when jointly implemented with the minimum wage policy.

ANALISIS TARIKKAN PERGERAKAN PENUMPANG ANGKUTAN PENYEBERANGAN PELABUHAN ULEE LHEUE KE ZONA PERDAGANGAN DAN JASA DAN ZONA PERKANTORAN DAN PENDIDIKAN

The city of Sabang is one of the most iconic tourism spots in Aceh Province. The Ulee Lheue Port is one of the most important accesses to Sabang, which is located in Banda Aceh City, the capital of Aceh Province. So that crossing activities carried out by both local residents and tourists will always pass through Banda Aceh. Along with the developments in each of these regions, the travel demand in a form of increased movement activity has been rising. The purpose of this study is to get a trip attraction model of Ulee Lheue Port ferry boat passenger that attracted to two destination zones in Banda Aceh by using multiple linear regression analysis methods and calculated by using SPSS software based on socio-economic and travel behavior data as independent variables (X) and the movement frequency as dependent variable (Y). Based on the analysis results, the model with each trip destination zone has a different travel behavior. The most influential variables for trip attraction model of the Ulee Lheue Port ferry passengers trip to trade and service zone are work activity (X12D), gender (X1), and income (X3). The most influential variables for the trip production model of the Ulee Lheue Port ferry passengers trip to office and educational zone are government workers (a type of work) (X6C), age (X2), gender (X1), crossing time (X11), private employees (a type of work) (X6D).

POLA PERGERAKAN TRANSPORTASI PENDUDUK YANG DITIMBULKAN OLEH KAWASAN PERUMAHAN DI KOTA MEDAN

Medan City as the location chosen in this study, has an area of 265.00 km2 with a population of 2,210,624 people and is increasing every year (Badan Pusat Statistik, 2015). With the increasing population of the city increasing activity and the number of good people traveling well. The city of Medan is currently promoting quite rapid development, a development from now on with the change in New settlements in the region which were built by settlement developers who also increased transportation development. It is very interesting to discuss about regional settlements in the city of Medan, especially in terms of trip generation. The housing area that is the object of research is part of land use in the Medan city area. From the research results, the best model that can be used at this time can be applied in housing location planning, namely the linear multiple regression model on luxury housing types, namely Y1 = 3.373 + 1.569 X1 + 0.139 X4 + 0.456 X6, therefore the findings that can be found are the variables that have an impact on the trip generation from housing. The value of the variable Number of family members (X1) is equal to 1,569 X1, it can be interpreted as increasing the value of the number of people who will increase travel production by 1,569 trips / family / day. The value of the variable number of motorcycle ownership (X4) is 0.139 X4 which means increasing the number of one unit will increase travel production by 0.139 trips / family / day. The value of the variable number of families attending school (X6) is 0.456 X6 which is related to an increase of one person increasing the production of trips that is equal to 0.456 trips / family / day.

An Efficiency Paradox of Uberization

Uberization promises to transform society based on an intuitive proposition: Advanced peer-to-peer matching guarantees greater overall efficiency. Here we show a paradox challenging this proposition in uberized ride-hail service, known as e-hail. By analyzing hundreds of local markets in Shenzhen, China, we discover e-hail is outperformed—in terms of wait time and trip production—by taxis hailed off street in areas with high densities of passengers and drivers. This paradox arises because a quicker match does not always expedite and enhance a service. On the contrary, it can induce competition that undermines the network effect, making a passenger less likely to benefit from more drivers, and vice versa, in e-hail than in taxi service. Consequently, simply attracting more users may not improve e-hail’s efficiency (defined as trip production at a given density of passengers and drivers), because its competitive edge diminishes with scale. The finding implies uberization has a limited impact on efficiency and is unlikely to create a “winner-take-all” in transportation.

The effects of traffic zoning with regular geometric shapes on the precision of trip production models

One of the first and most critical steps involved in transportation studies is to determine the traffic zoning scheme. A traffic analysis zone (TAZ) is the main unit of the transportation analysis. In most transportation planning studies, the focus is only on the modeling process, while ignoring aspects such as TAZ definition, configuration, and design. Although the definition of TAZs is one of the key stages in transportation planning studies, a proper approach for the definition is still one of the unsolved issues.In this research, a TAZ with regular geometric shapes, called regular traffic analysis zones (RTAZs), is presented as a new approach to traffic zoning. On the other hand, the accuracy of calculating trip production, as the first step of well-known 4-step transportation demand modeling system, is of high necessity, because this step determines the total number of trips. In the current study, the effects of RTAZs on the results of trip production models (TPMs) for the city of Khoy in Iran were investigated. The results indicated that TPMs based on regular zoning system acted almost similar to traditional zoning system in estimating produced trips and, in some cases, even presented relatively better estimations. Regarding the difficulties encountered in the topic of the traditional traffic zoning system, the present work showed that RTAZs facilitated the modeling process and led to time and cost savings.

Paired-Line Hybrid Transit System in a Monocentric City with Exponential Demand Pattern

This paper analyzes the design problem of a paired-line hybrid transit system in a monocentric city with a densely populated central business district (CBD). The trip production rate is assumed to decrease exponentially with increasing distance from the city center. As to the trip distribution, two different areas, the CBD and the rest of the city, are identified. Trips ending in each area are homogeneously distributed within that area but their proportions in the total trips are distinctive to model the heterogeneity in trip distribution. To address the challenge of estimating system costs analytically under the above exponential demand pattern, new approximation methods are proposed and validated using a Monte Carlo simulation. Results of numerical experiments show that the exponential demand pattern helps reduce the cost of paired-line hybrid transit systems in monocentric cities, with a saving up to 20% when both the trip production and distribution are heavily concentrated in the CBD. Furthermore, strong quadratic/linear relationships are found between the parameters controling demand concentration level and the system cost. The proposed model can guide the design of hybrid transit systems in monocentric cities with a demand pattern similarly structured as considered in this study.

Paired-line hybrid transit design considering spatial heterogeneity

This study attempts to incorporate spatial heterogeneity into the optimal design of paired-line hybrid transit systems, which aims to strike a better balance between accessibility and efficiency by leveraging the flexibility of a demand adaptive service. A simple trip production and distribution model is introduced to differentiate the central business district (CBD) of a city from its periphery. To cope with the heterogeneous demand pattern, the transit system is also configured differently inside and outside the CBD, for both its fixed route and demand adaptive services. Allowing the supply heterogeneity complicates transit users’ route choice modeling considerably. As a result, user costs must be estimated separately for six subregions that constitute the feasible set of the fixed route headway. Each subregion corresponds to a unique route choice behavior, hence leading to a distinctive design model that is formulated as a mixed integer program and solved by a commercial solver. Results of numerical experiments show that concentrating demand in the CBD significantly reduces the average system cost, and this benefit increases as the average demand density becomes larger. Also, recognizing demand heterogeneity and responding to it with a tailored design can be highly beneficial. However, this benefit diminishes as the average demand density increases.

Analysis of trip generation rates in residential commuting based on mobile phone signaling data

In this paper, mobile phone signaling data are first processed to extract information such as the trip volume and spatial distribution from the starting point to the termination point. This information is then used to identify the residential and employment locations of users. Next, multiple Thiessen polygons based on cell towers are aggregated into Traffic Analysis Zones (TAZs) to minimize differences between the actual cell tower coverage and the theoretical coverage. Then, based on TAZ cluster analysis involving transport accessibility and commuting population density, multiple stepwise regression is applied to obtain the commuting trip production rates and attraction rates for overall residential land and each subdivided housing type during the peak morning hours. The obtained commuting trip generation rates can be directly applied to local transport analysis models. This paper suggests that as information and data sharing continue, mobile phone signaling data will become increasingly important for use in future trip rate research.

Comparative Assessment of Industrial Classification Systems for Modeling Freight Production and Freight Trip Production

Freight demand models typically employ a priori classification systems for dividing establishments into hypothetically complementary groups with homogeneous patterns in freight production (FP) and freight trip production (FTP). Although an attractive and popular notion, the assumption of homogeneity within these a priori industrial classes is reductive in nature and is not yet tested in literature. This research examines this hypothesis and explores the possibility of a data-driven segmentation by examining the relationships between FP/FTP patterns and prevalent a priori classes; subsequently, it creates homogeneous ensembles of a posteriori segments through aggregation. This research labels, explains, and interprets these novel segments using commodity value density of industrial classes. The alternate segmentation schemes are compared in their ability to predict FP and FTP and it is found that: (i) industrial classification systems (NAICS, ISIC) perform significantly better than product classification systems (ASICC); (ii) a considerable portion of variability in FTP does not depend on employment predictor due to the underlying influence of shipment size; (iii) an a posteriori segmentation scheme considering shipment size may represent an effective middle ground for developing both FP and FTP models in freight demand model systems. Adoption of these novel segments of the freight travel market has the potential to reduce the sample size requirements of freight demand model systems and minimize the financial necessities for future freight surveys.

Paired-line hybrid transit design considering spatial heterogeneity

Abstract This study attempts to incorporate spatial heterogeneity into the optimal design of paired-line hybrid transit systems, which aims to strike a better balance between accessibility and efficiency by leveraging the flexibility of a demand adaptive service. A simple trip production and distribution model is introduced to differentiate the central business district (CBD) of a city from its periphery. To cope with the heterogeneous demand pattern, the transit system is also configured differently inside and outside the CBD, for both its fixed route and demand adaptive services. Allowing the supply heterogeneity complicates transit users’ route choice modeling considerably. As a result, user costs must be estimated separately for six subregions that constitute the feasible set of the fixed route headway. Each subregion corresponds to a unique route choice behavior, hence leading to a distinctive design model that is formulated as a mixed integer program and solved by a commercial solver. Results of numerical experiments show that concentrating demand in CBD significantly reduces the average system cost, and this benefit increases as the average demand density becomes larger. Also, recognizing demand heterogeneity and responding to it with a tailored design could be highly beneficial. However, this benefit diminishes as the average demand density increases.

Penggunaan Metode Goal Programming Untuk Optimasi Perancangan Proses Produksi Produk Wingko Babat

One form of actual problems in the manufacturing industry is how to maximize the value of profit with various constraints or constraints. The problem is not only experienced by big industry but also small industry like in UD. Wingko trip Mr. Moel who is trying to maximize the value of profit by estimating the amount of wingko trips that must be produced because there is a problem of the difference in the amount of production with one of the limits associated with the production capacity. The resulting production difference causes the amount of wingko trip or excess production in certain month. The method of Goal Programming is the one method of optimization with more than one objective that are interconnected with each other or conflicting to solve the problem. This research uses the Goal Programming method to optimize the production amount and maximize the profit of the product, and to forecast the number of forward demand by using the number of the past request to know how many products must be produced. Goal Programming Formulations use forecasting as decision variables for optimization of results in compliance with criteria and constraints. The results show the optimal amount of production in January - December 2017 are 29860 bag wingko original, 24000 bag wingko chocolate chocolate, 26004 bag wingko tripe jackfruit and 18084 bag wingko tripe durian with total profit in 2017 is Rp. 429.736.600.Â

Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.

The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets

The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result indicates the imbalanced spatial and temporal demand of bike sharing trips. The long short-term memory neural networks (LSTM NNs) were then developed to predict the bike sharing trip production and attraction at TAZ for different time intervals, including the 10-min, 15-min, 20-min and 30-min intervals. The validation results suggested that the developed LSTM NNs have reasonable good prediction accuracy in trip productions and attractions for different time intervals. The statistical models and recently developed machine learning methods were also developed to benchmark the LSTM NN. The comparison results suggested that the LSTM NNs provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals. The developed LSTM NNs can be used to predict the gap between the inflow and outflow of the sharing bike trips at a TAZ, which provide useful information for rebalancing the sharing bike in the system.

Bike sharing differences among Millennials, Gen Xers, and Baby Boomers: Lessons learnt from New York City’s bike share

There is a growing literature on the changing travel patterns in the United States. The changes are largely driven by the emerging shared-mobility services and the travel behavior of the younger generations. This study builds on an investigation of Millennials’, Gen Xers’ and Baby Boomers’ bike sharing ridership in New York City. This study examines station-level bike share use focusing on whether and how the effects of land-use and built environment vary across different population segments. Using New York’s Citi Bike system data, we develop zero-inflated negative binominal models to estimate hourly trip productions at stations for five age cohorts: younger Millennials (born 1995 to 2000), mid Millennials (1989 to 1994), older Millennials (born 1979 to 1988), Generation Xers (born 1965 to 1978), and Baby Boomers (born 1946 to 1964). Consistent with the literature, our results suggest that weather related variables, land-use and built environment characteristics have significant effects on the overall bike sharing usage. Our findings also reveal variations across age cohorts. For example, intersection density is positively related to younger Millennials’ bike share trip production. However, this factor is not statistically significant for other age groups. Our findings provide valuable insights for planners and policy-makers, and set the basis for improving the understanding of cohort differences in bike sharing demand.