With the vigorous promoΩ↓tion of smart city constructπ↔ion, smart traffic, smart med¥$☆ical care, smart security, smart ener∏¥€gy, etc. are all vigorous"✔δγly concerned. Urban constr<<uction, traffic firsΩε∑t, and intelligent transportation have ÷÷αφbecome important driving forces δ∑≤for economic development. They ↔ have also become more α★®integrated into all fields ★©of social life. Theyλ₩ have changed people‘s≠∞¥← lives and working methods and p✔✔∞♥layed an important role in t&¶λhe smart urban construction. The follo¥"✘wing is the Shenzhen C÷✔Ω♥ity Planning and Design Cen÷> ter for Urban Transportation Research ©♥Institute (Intelligent "≈ Institute) Shaoyuan "wisdom f♠↕or the future of the city transport soδlutions | to create&q×¥∞uot; 4C city "" a↕✘βrticle.
Since its establishment 20 years a±'☆go, Shenzhen Traffic Ce☆"σnter has been devoting itself to R∞ & D and application of traf&☆εfic model and traffic big data to carry '€< out planning and design of urb₩αδan traffic and intelligen$εγt traffic. In recent ≤₹years, we have transfor≠¥¶med from a traditional planning andσ€δ™ design institute to a full-fledged≥₽©" provider of urban transpo§ rtation solutions. What we× ♣ report today is a preliminary refl♠☆ ection on the overall scheme of≤←♥ smart traffic for the future city®≠✔. Divided into two parts, fi•¶€rst of all, the future w•&isdom of the city, the vision of ♥smart traffic, the second i★' &s a preliminary refl₩φ✘ection.
From the entire process of dπ®evelopment of smart traffi§↔¥✘c point of view, can be divided into th"↑ree stages of development. 1.0®☆ stage We focus on the development of a←§↓ single product and f ®÷unction of the applicationα design. 2.0 phase focus on the interco ≤"↔nnection of big data, breaking the ∑→data barrier. We are entering↑<∑÷ the new smart city 3.0 phas÷∏★e, which is a new smart city develop✔¶>↕ment stage, based on the all-thin &gs-based service-oriented new smart c♦"ity, emphasizing public participat">ion, government-enterprisΩε♥e cooperation.
McKinsey‘s research provide→↔↓s a very comprehensi∞™₹•ve overview of future¥<♥ traffic trends in sev₹→en areas, including shared mobil↑♣φ¶ity, automotive electrifica ®>tion, autopilot, new ≈←' public transport, renewable energy, ne§'$w infrastructure and th✘↓♠e Internet of Things, covering the¥ core of the future On the one hand¥<♥, in the future, intelligent transpoφ≈↔rtation is based on the data-d&¥™riven interconnection ofΩ± all things and meanwhile various new α transportation modes are used ∞>as carriers to organi ↓ze new modes of transportatε↓≈ion. The new transportaλ₽§tion services are reflected in shared≥♥§ mobility.
The United States mentioned in the λ✔∏"Emerging Technology T'¶rends Report 2016-2045" that th±ε∏↓e Internet of Things, dat•₽a mining, and technology≠β including blockchain may fundamentally↑≈ change the travel mode of our entire cδ&€₽ity traffic in the next 10-20 yearsε±. Therefore, the entire city The tr∑∞ ≠ansportation industry is also cons©<σtantly changing.
Focus on the construction of sma£∑✘rt cities in Europe and↕σ the United States, the core o✔ ₽f development is the construction of&♦ the four major systems, including th ₽φαe wisdom of the perception system, sm<≠art decision-making, sma↔≠ rt operations, smart serviε≤←ces in four areas. I← n the future, city transport must ha☆'★ve four key features <∞in all
The first feature is that the futur♠βe-oriented urban transp↔ortation is a complex gian€ t system. Under this system, it is ©¶necessary to build a system of a₩ λll-inclusive, interconne←§cted, diversified and multi-☆®₩§dimensional systems.
The second is that urb×®α"an management has shifted ε±from passive management i≠≈≥✔n the past to smart governance. Since 2≠∏→$000, Shenzhen has emphasized the sma¶¥∏rt growth of urban transport and th¥∞e concept of smart governance. The preγ©mise of smart governance requires the ♦÷∞support of big data, an÷¥d precise data control based on big dat™↑a Strategy and service measures.
For example, the spec£★∑♥ific road through big data to under♠♠stand what kind of vehicles∞&₽ use our roads, different time and spγγace for what factors sensiti♦ve to the precise introduction of poli≥Ω≠cies to achieve road network c ♠onstruction purposes.
The third aspect is char•>←acterized by the mobility concept p✘↓resented by the EU, '↑which has several cor÷↓e features.
The first is that we are co πncerned about the objective of moving f♥¶rom focusing on improving th≥₽✘™e capacity of transportation fλ&βacilities and moving at a♥® faster pace to focusing on people♣¶✔-oriented accessibility, including λ&✔changes in urban life, health andα↔₽± environment, and sup×€port for economic via trans §♥port.
Second, cities in the future will place>δ more emphasis on "urb₽®an governance" rather tha&☆n "urban manageme♥¶≤nt," emphasizing the tranφsformation of government services, serv∑↑ ice coordination and the creationε£₩ of social values. Urban developmen€λt highlights smart mana≈×₽gement and smart growth.
Third, the way of thin±¶king is changed. The traditional thinki£ ng focuses on the independent system ♣↕↕<construction. The new thinking focuses™ ∞ more on the coordinated developmen¶ε t between systems. The focus is on←π₽ the coordination of✔σ™ interests, especially → the public participation. Base↕Ω≈d on the big data itself can be a ™chieved from the planning to the prepa ♥÷ration of the entire pro ÷∑cess of precision calibration and c↔ πlosed-loop management, the fπ$ormation of a more effective gov☆≤λ↓ernance model.
The fourth is to serve as t®φhe core, reflected in the trave®γl experience of the people, focusing&φ on people‘s feelings as th"&§αe core of the multi-objective const<↓α ruction, especially for the pe♣≠€rsonal experience of the whole process ÷€of seamless travel services•®→&.
Based on the above four trend£∞₽£s, cities in the futur∏♥εe will surely become perceptible, opε>β≥erational, manageable and serviceable®• cities. These four cities embodΩ§∞y urban development as the &qu'✘ot;4C city", namely Perception Ci↕™₽ty, Deduction City, Ma§πnaging City and Serving∑✘ City online.
First, holographic perce÷∞ption of the city. It is n↑ ecessary to build a multi-≠Ω≤≈level perception system baseΩ✘αd on big data of spat§✔ial units, including intell±₹'™igent intersections and smart road sγ>↑ections so as to realize multi-level,♣★ full-time and accurate lane awareness.↕ In the past, The weather and the en∞∑λtire traffic environment perceptio€₩ n system. Shenzhen uses th§∏ ₩e wisdom lamppost, wisdom intersec§ tion, wisdom pavement®≠Ω≥ and other elements to jointly buil♦¥÷₹d a set of new gener π£ation of wisdom road perception ®π₽system. Wisdom Pole has maγ↔βny functions, including high-d≈ ♦≥efinition video, tra屩☆ffic detection and infor±₽mation release, can realize intelligen☆♠π₹t monitoring, traffic flow "←detection, road dang↓∞∑er identification, information exchπ ange, multi-target rad®≠Ωar tracking and othe×£r functions. This is one of the co★£♦re carriers of traffic hologσγ☆™raphic awareness system in •₹≠the future.
The second is to dedu✔€ce the city online. Based on big d♣♠ata technology to achieve traffic tr"★aceability technology, a>㥕 profound understandin↔§g of the various types of traffic g×✘™εeneration and evolution mechanism. For ©£₩≥example, this is through♣♥> cell phone signaling data analysis of >←the composition of different arσβ €ea staff. The map can be shared by £←₽ cycling dynamic data₩ detection to understand the ≥♣✘§use of their last mile, including↔β≥ the 24-hour monitoring of the flow™•♣ of people scattered around the •↑area.
Big data and deep learning techniques ©✘have a large number ®™₹∞of applications in the entire tra™≥©ffic texture analysis, traffic pra'>ctice discovery, public opini♠÷©•on analysis, police inspections ∞☆ ↓and so on. In addition, th©↕πe establishment of online deducti∞∏¶ on system, through the data regre>±≤ssion of the entire closed-loop acti®€™×vities. Shenzhen core area online→π simulation system to do a trial, in$∏ the driveway above the layou> t of a large number of s₽★¶ensing systems, including h≈✘↑igh-definition video, through the ≤§ layout we can accurately find each®€ vehicle in the backgrou₽₹•nd traffic inside the brain ∏ &↕can be realistic to restore the entire¥π♥ real-time traffic flo→βδ↑w , Make the deduction of traf←φfic plan, including the o☆>✔$rganization plan of traf<ε<<fic, make a systematic support for≥&✘ the optimization of the whole traffic§¶€ flow.
This is the actual case. The traffi₩∏∑c police made use of the online simula奶tion system in the acci ™₹dent of a tunnel in Shenzhen. Thr€>♣'ough the real-time onlin•δ₹↓e deduction of this s↕™¥↕ystem, it can ease the traffic in theεπ upper reaches and effΩ↑ ectively solve the problem ₹¥"within 10 minutes. In the absence o© φf the system in the past,©∞ congestion may last more than hσ alf an hour. This is the ♥↓case discussed at th♥€<←e on-the-spot meeting of the Chinese ✔ $σpublic security traffic police this π₩€year in Shenzhen.
Third, smart control of the ci×'₩ty. It is to construct a closed-loo§∏$p management and control ¥βactivities of "planning-d₹ββesign-construction-management-data&quo×♣t; collaborative operβ±ation, and make a brief intr£δ oduction from the three aspects of∏∑≠÷ regional level, city leve←→♥l and campus level.
At the core of the reg <←•ional level is the establishmen&¥♦t of a regional-level manageσ✔¶ment and control strategy and system ∏&for active demand control. In σλ¶ Arizona, the United States pr♥¥÷♦ovided programs to different groupו₩s of people, different tr¶εavel times and differenδ¶"t travel expenses. By trying to e§← ffectively change the behav≥∏&ior and plans of 20% of the ×♣travelers, the United >♦≤States achieved a balance of ₽♥≠ time and space on the± road network.
粵公網安備 44031102000295号 ✔☆★≈; 京ICP證000000号 
