Traffic Demand Forecast Research Articles

Improving Bus Passenger Flow Prediction Using Bi-LSTM Fusion Model and SMO Algorithm

Bus passenger flow prediction integrates data analytics and modelling techniques to forecast the number of passengers using bus services, incorporating historical usage patterns, demographics, weather, and events for optimal scheduling and resource allocation. The Bi-LSTM fusion model enhances accuracy by processing past and future features simultaneously, leveraging bidirectional LSTM layers and an attention mechanism to capture temporal dependencies. This approach not only refines insights crucial for urban mobility challenges like traffic management and demand forecasting but also improves route planning and service efficiency. The SMO algorithm initializes with a diverse spider monkey population exploring solution spaces. Through local and global leader phases, it iteratively updates positions based on fitness and probabilistic selections, maintaining a balance between exploration and exploitation. Perturbation-based updates in the local leader phase ensure adaptability, preventing premature convergence, while the global leader phase guides towards better solutions, enhancing efficiency in complex optimization tasks and promoting dynamic adaptation. In Dataset 1, the proposed model achieved a training time of 137 seconds, slightly longer than HA (115s), SARIMA (112s), GRU (123s), and ST-ResNet (113s). It demonstrated superior accuracy at 89%, surpassing HA (66%), SARIMA (68%), GRU (63%), DeepST (78%), and ST-ResNet (84%). In Dataset 2, the model exhibited the lowest RMSE, MAE, and MAPE%, indicating superior predictive accuracy over SVR, CNN, GCN, LSTM, and CONV LSTM models. These findings validate the proposed model's effectiveness in enhancing predictive capabilities for transit forecasting, underscoring its potential to optimize urban mobility and transportation management strategies significantly.

Improving deep-learning methods for area-based traffic demand prediction via hierarchical reconciliation

Mobility services require accurate demand prediction in both space and time to effectively manage fleet rebalancing, provide quick on-demand responses, and enable advanced ride-sharing with minimum fleet size. Although the optimization of mobility services is a widely studied topic, the demand prediction side has received relatively less attention. In this paper, we aim to develop an efficient method for traffic demand forecasting by means of deep learning and hierarchical reconciliation approaches. The concepts, as well as the theories behind the proposed approach, are founded on Hierarchical Time Series (HTS), which also adopts Long Short-term Memory (LSTM) as a special kind of Recurrent Neural Network (RNN) to learn from the associated time series and produce reliable demand predictions. The proposed approach relies on the proper design of the HTS structure to get accurate forecasts for the number of trip departures and their associated uncertainty within predefined zones and aggregated collections of these zones. Moreover, the error analysis is essential for accomplishing the reconciliation in the HTS structure optimally. The proposed approach, consisting of deep learning, error analysis, and optimal reconciliation stages, has a remarkable ability to predict demand and control the forecasting quality at all levels of the hierarchical structure. We evaluate the proposed approach using a large-scale GPS tracking dataset from Lyon, France. The proposed method reduces the root mean square error (RMSE) by 13.92% and 14.77% for predefined and aggregated zones, respectively, compared with the LSTM using the historical demand and external features of time at the quarter-hour time step. Similarly, the corresponding improvements in mean absolute percentage error (MAPE) are 14.87% and 19.23%, respectively.

An Analysis of the Perspective Road Design Scheme Around Zhangzhou Olympic Sports Center

City events take place in the Zhangzhou Olympic Sports Center, resulting in traffic. Hence, this study analyzed the road planning and design around Zhangzhou Olympic Sports Center through traffic demand forecasting. The simulated data and road plan underwent macro prediction model technology and numerical simulation technology through Emme and TransCAD. The regional road network structure, traffic demand forecast, plane, longitudinal section, and cross-sectional program selection were investigated to determine a reasonable design program. While urban development programs were implemented, the study ensured an increase in the city's economic growth alongside the guaranteed value of citizens’ welfare.

Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

Traffic demand prediction based on spatial-temporal guided multi graph Sandwich-Transformer

The ability of spatial-temporal traffic demand prediction is crucial for urban computing, traffic management and future autonomous driving. In this paper, a novel Spatial-Temporal Guided Multi-graph Sandwich-Transformer (STGMT) is suggested to address the ubiquitous spatial-temporal heterogeneity in traffic demand forecasting. Compared to the original Transformer, we employ Time to Vector (Time2Vec) and Node to Vector (Node2Vec) in the embedding layer to obtain universal representations for temporal nodes and spatial nodes, respectively, which are then combined to form Spatial-Temporal Embedding (STE) blocks. The STE guides the attention mechanism, maintaining a unique parameter space for spatial-temporal nodes and enabling the learning of node-specific patterns. In STGMT, we develop Multi-head Temporal Attention (MTA) and Multi-head Temporal Interactive Attention (MTIA) for extracting temporal features, while Multi-head Spatial Attention (MSA) is employed for extracting spatial features. Furthermore, MSA incorporates both the accessibility graph determined by road topology and the similarity graph determined by specific traffic events to characterize the pairwise relationships among spatial nodes. Various attentions and feed-forward layers are rearranged and combined to form the Sandwich-Transformer. Extensive experiments are conducted on public datasets of node-level tasks of two different types (highway and urban) and indicate that the STGMT outperforms state-of-the-art models. The proposed STGMT effectively addresses the ubiquitous spatial-temporal heterogeneity challenge in traffic demand forecasting, thereby enhancing the accuracy of traffic demand prediction and offering valuable guidance for traffic planning and operations. Our code and data are open source at https://github.com/YanJieWen/STGMT-Tensorflow-implementation.

The Impact of Taxiway System Development Stages on Runway Capacity and Delay under Demand Volatility

This study aims to determine the impact of the International Civil Aviation Organization’s (ICAO) taxiway system development stages on runway capacity and delays in a single-runway airport that serves mixed operations by using a combined approach integrating the capacity-demand estimation analyses. We proposed a generic and systematic approach to taxiway development alternatives based on the ICAO guidelines rather than case-specific improvements presented in previous studies. The Samsun Çarşamba Airport (LTFH), which has the most basic level of taxiway layout, was chosen as a case study. The development stages were modeled and run in a fast-time simulation environment for different traffic scenarios based on historical data. As a result of modelling, average airborne and gate waiting delay of arrivals, queue delay of departures, taxi times, and runway occupation times were obtained. In addition, gate sensitivity and cost-benefit analyses were carried out. A triple exponential smoothing forecast was performed for different scenarios including normal, runway closures, COVID-19, and recovery from COVID-19. Practical capacities of the stages were revealed using the results of simulation and traffic demand forecasts. In addition, we evaluated the potential operational improvements of the stages by modifying a previously presented mathematical model. The proposed approach can assist airport operators in predicting the airport capacity saturation and selecting the best capacity improvement strategy for unexpected events.

A Study of Traffic Demand Forecasting and Traffic Impact Assessment for location of large facilities

大型施設の立地を検討する場合には、施設が呼び込む交通需要による影響分析を行い、周辺道路や施設への影響が軽微であることを示す必要がある。施設立地により発生する交通需要は、様々な要因によって大きくその性質・規模等が異なるため、検討施設の特性を考慮した影響評価手法を選定する必要がある。そこで本稿では、論文の前半で、交通影響評価に必要となる調査・分析を実施する際に検討すべきポイントや留意点を分析の基本的な流れに沿って示す。後半では、交通影響評価手法の一つである「交差点解析」に焦点を絞り、解析精度に大きく影響を及ぼす「飽和交通流率」の算出方法を事例とともに複数紹介し、各手法のメリット・デメリットを整理したうえで、検討内容や検討段階、業務全体の方向性を見据えた適切な計算手法を選定することの重要性を示した。

Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model

Road traffic congestion is a common problem in most large cities, and exploring the root causes is essential to alleviate traffic congestion. Travel behavior is closely related to the built environment, and affects road travel speed. This paper investigated the direct effect of built environment on the average travel speed of road traffic. Taxi trajectories were divided into 30 min time slot (48 time slots throughout the day) and matched to the road network to obtain the average travel speed of road segments. The Points of Interest (POIs) in the buffer zone on both sides of the road segment were used to calculate the built environment indicators corresponding to the road segment, and then a spatial panel data model was proposed to assess the influence of the built environment adjacent to the road segment on the average travel speed of the road segment. The results demonstrated that the bus stop density, healthcare service density, sports and leisure service density, and parking entrance and exit density are the key factors that positively affect the average road travel speed. The residential community density and business building density are the key factors that negatively affect the average travel speed. Built environments have spatial correlation and spatial heterogeneity in their influence on the average travel speed of road segments. Findings of this study may provide useful insights for understanding the correlation between road travel speed and built environment, which would have important implications for urban planning and governance, traffic demand forecasting and traffic system optimization.

Traffic demand estimation using path information from Bluetooth data

Recent advances in technology have made available numerous new monitoring systems that collect updated traffic measurements both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities for Origin-Destination (OD) traffic demand estimation and forecast. Therefore, the aim of this paper is to study how to exploit available information detected by new monitoring devices in the estimation of traffic demand. Starting from the formulation proposed by Spiess (1987, 1990), in this paper a new method to estimate the traffic demand by means of Bluetooth data is proposed. It explores inherent properties of this information in an off-line (and static) context, where mathematical formulation of the estimation problem can be derived. The effectiveness of the proposed method has been investigated in an extensive plan of experiments carried out both on test networks and on a study network consisting in a part of the city of Rome, Italy, obtaining promising results in both applications.

A critical overview of four-stage model under the background of the rise of ride-sharing

Future transport systems are expected to rely to a much greater extent on intelligent ride-sharing services, thus ride-sharing will have a significant impact on traffic planning and demand forecasting. This paper aims to review the classic transport planning four-stage model critically and extend it to make it more appropriate for traffic planning in the context of the rise of ride-sharing.

Telecommunications network design: Technology impacts and future directions

AbstractDuring the past 50 years telecommunication network technology has raced ahead powered by exponential advances in computer chip and fiber optic technology. The introduction of packet‐switched networks and the Internet has increased network traffic demands exponentially and has nurtured a variety of new telecommunication services (e.g., email, web‐surfing, social media, streaming services, e‐commerce, the Internet of Things, etc.) over the past 30 years. This rapidly changing environment (along with certain technical considerations) has made it very difficult to give accurate forecasts of traffic demands for both the short‐term (intraday) and for the longer term (year‐to‐year). Telecommunications network design has evolved from the voice‐only networks of the 1970s where operations research (OR) methodology was indispensable to current multiservice packet‐switched data networks whose uncertainty and rapid evolution has reduced the utility of classical optimization models that rely on accurate traffic demand forecasts. After summarizing the major developments in telecommunications network design technologies/services, we describe some promising future research directions to address an environment where technology is ever‐changing and traffic forecasting is difficult. In particular, we focus on the public Internet and emphasize possible work where OR/optimization methodology may be useful in understanding and solving telecommunications network routing and design problems.

Air quality and fossil fuel driven transportation in the Metropolitan Area of São Paulo

In this paper, we use origin and destination mobility surveys and high-resolution assignment and emission models to study air quality and short-lived climate pollutant impacts related to on-road transportation in the Metropolitan Area of São Paulo (MASP). To begin with, we calculate transport carbon dioxide (CO2) emissions from fossil fuel-driven vehicles (light and heavy-duty) at spatial and temporal resolutions of 500 m and one hour, by means of traffic demand forecasting. These estimates, carry out for 2007 and 2012, are based on passenger and freight trips and the height of the atmospheric boundary layer, among other variables. These proxies depend also on ancillary parameters as particulate matter concentrations and dilution rates. In the second place, we evaluate the changes in CO2 emissions from the MASP (3%/year). Transport emission inventories combine mobility surveys and road network assignments with air quality data. In spite of using different methodologies, bottom-up road link assignments versus top-down vehicle activity-based and fuel consumption approaches, the estimated CO2 emissions are consistent with the Official São Paulo State's Inventory. This work found that the CO2 emissions in MASP were 10,044 and 11,503 t Ceq./day in 2007 and 2012 (73% light and 27% heavy vehicles), respectively. On-road emission patterns agree with the spatial-temporal variation of transportation journeys and corresponding passenger and freight network assignments. Temporal patterns, diurnal, weekly and monthly, were determined using traffic counts and congestion surrogates. The patterns were also crosschecked with average CO2 measurements, available for 2014 at the road (western area of MASP) and background sites (Jaraguá Peak). Kerbside road measurements showed two prominent peaks associated to the morning (437 ± 45 ppm) and night rush hours (435 ± 49 ppm), coupled to low values of boundary layer height (313 m) and dilution rate (329 m2/h). Background values (414 ± 2 ppm) were subtracted from on-road measurements to estimate excess CO2 (12 ± 8 ppm) directly attributed to vehicles. The inventory reflects the relationships between traffic patterns and emissions, and the developed methodology could be used to evaluate the impacts of forthcoming urban transport and emission control policies. In the future, our estimates will be verified with ground measurements of CO2 concentrations over a bigger monitoring network in the MASP.

Traffic demand Forecast of online car-hailing based on BP Neural Network

To accurately predict passengers’ demand for ride-hailing, increase transport capacity in some areas directionally, make it easier for passengers to book ride-hailing, and thus enhance passengers’ travel experience, based on BP neural network model, combined with 2015-Mak-2019 ride-hailing demand change data, and based on MATLAB platform, the demand trend of 20202024 is forecasted. The results show that the demand for shared cars and rides will increase rapidly. Demand for shared bikes and taxis is rising slowly.

Joint Demand Forecasting and DQN-Based Control for Energy-Aware Mobile Traffic Offloading

With the explosive growth in demand for mobile traffic, one of the promising solutions is to offload cellular traffic to small base stations for better system efficiency. Due to increasing system complexity, network operators are facing severe challenges and looking for machine learning-based solutions. In this work, we propose an energy-aware mobile traffic offloading scheme in the heterogeneous network jointly apply deep Q network (DQN) decision making and advanced traffic demand forecasting. The base station control model is trained and verified on an open dataset from a major telecom operator. The performance evaluation shows that DQN-based methods outperform others at all levels of mobile traffic demand. Also, the advantage of accurate traffic prediction is more significant under higher traffic demand.

Research on dynamic prediction method for traffic demand based on trip generation analysis

The characteristics of the residents’ travel in the information age have changed. The existing urban traffic demand forecast is mainly proceeded using the land property. Based on the main contents of urban residents’ travel survey and the characteristics of traditional residents’ travel demand, this article analyzed the dynamic changes of travel characteristics and the main influencing factors of travel formation of future residents. Combined with the travel influence factor weight of travel generation forecast stage established by the analytic hierarchy process, such as the land use, travel mode composition and travel choice, the location influence coefficient in the model of population, land use, and travel generation in city was modified to characterize the dynamic state of travel demand of residents in the phase of travel generation stage. Then a “dynamic” method for forecasting and analyzing traffic travel demand was put forward to apply to the prediction and evaluation of travel demand in Guilin. The results showed that it can reflect the dynamic characteristics of residents’ travel compared with the traditional travel demand prediction.

Spatial Interpolation of Missing Annual Average Daily Traffic Data Using Copula-Based Model

Accurate estimation of traffic counts [(i.e., annual average daily traffic (AADT)] is essential to transportation agencies for traffic demand forecasting, emission evaluation, pavement design, and project prioritization. Traditional AADT estimation methods rely on either temporal data imputation techniques based on historical records or kriging-based spatial interpolation approaches. However, Kriging method utilizes the correlation function as the sole descriptor of spatial dependency, posing limitations to yield accurate interpolation results for unstable AADTs under complex traffic patterns due to diverse road functions or land uses. This study proposed a copula-based model that combines spatial dependency and marginal distribution for missing AADT interpolation to weaken the limitation of Kriging method. Thus, the proposed model not only can describe the spatial dependency but also is robust to outliers. AADT data collected from the California state highway network were used to evaluate the effectiveness of spatial copula models with varying missing data rates. Four road segments with regular and recreational traffic patterns were selected to compare with existing kriging-based approaches. Results suggested that the spatial copulas yielded significantly higher accuracy rates than kriging did for irregular travel patterns with high missing data rates. Spatial copula models hold a great potential to improve the performance of large-scale transportation network-wide data imputation for planning and operational usages.

Improving the approaches of traffic demand forecasting in the big data era

Since the 2000s, an era of big data has emerged. Since then, urban planners have increasingly applied the theory and methods of big data in planning practice. Recent decades illustrate a rapid increase of the application of big data approaches in transportation, bringing new opportunities for innovation in transport modeling. This article analyzes the theories and methods of big data in traffic demand forecasting. In view of theory, the new models and algorithms are proposed in order to adapt to new big data and response to the limitations of traditional disaggregated approaches. In such approaches, three traffic demand-forecasting methods, the full sample-demand distribution model, the traffic integration model, the model organism protein expression database model, are discussed. Undoubtedly, the development of big data also presents new challenges to travel-demand forecasting methods regarding data acquisition, data processing, data analysis, and application of results. In particular, identifying how to improve approaches to traffic-demand forecasting in the big data era in the Third World will be a challenge to the researchers in the field.

Queuing analysis using Viswalk for check-in counter: Case study of Lombok Praya International Airport

Queuing analysis on check-in counter in airport is required to accommodate the passenger processing facilities, such as check-in service. The purpose of this paper is to evaluate the performance of a queuing system at check-in facilities in 2nd passenger terminal Lombok Praya International Airport in 2016 and 2036. This analysis is done by determining parameters of the queues at every check-in counter through simulations on Viswalk. Before running the simulation, the model of airport terminal must be created first. Queuing simulation starts with the traffic demand forecasting of domestic departure passenger for the next 20 years. To determine the optimal number of check-in counters, an iteration of various number is performed. Check-in services time data were adopted from previous study which became an input of the simulation using two different scenarios. The expected output are the queuing parameter and the distribution of passenger service time at each check-in counter.

Land Use-Slow Traffic and Demand Forecasting

In the face of solving the urban traffic congestion problem radically, emphasis has been laid on the research on slow traffic planning of urban built environment. Hence, research on slow traffic demand forecasting can provide a basis for the planning of urban slow traffic systems. Based on land use, the overall planning of the new Guangming (GM) district, and the population prediction results, the slow traffic demand within the scope of the new district was forecasted by combining the per capita trip frequency, and the spatial distribution of the slow traffic flow of the new GM district was forecasted per the forecasted demand quantity for slow traffic. The following research conclusions were obtained. Within the new GM district, the correlation of the total demand for slow traffic with the land use functions and population distribution was high, and the cross-zone traffic was mainly decided by the land usage of this district. The cross-unit slow traffic flow was concentrated in the Gongming central, Guangming central, high-tech zone, and Yutian zones. This research provides a guideline for the layout of slow traffic facilities in the future.

A Public Traffic Demand Forecast Method Based on Computational Experiments

The conventional mathematical models that are used for traffic distribution and traffic mode choice forecasts consider neither the individual heterogeneity on the micro level nor the changeable traffic scenes. This prompted us to propose a new forecast method composed of a traffic survey, an artificial transportation system (ATS), and computational experiments. We introduced a BDI modeling method in the agent-based ATS. This method considers an individual's psychological characteristics in combination with logical thinking, which was introduced to individual passenger agents, to deduce each passenger's decision-making process when choosing the traffic mode and route. A series of computational experiments were conducted on the ATS by using a school bus system as a case study to validate the feasibility and superiority of our method. Several computational experiments were conducted to predict the traffic distribution in normal and abnormal traffic scenarios and to analyze the extent to which each factor influences the travel modal split. Furthermore, the outcomes of various vehicle-scheduling plans were predicted and analyzed by using computational experiments to determine the optimal plan and support the establishment of transportation policies in the real world.